Package: mealpy
from openpyxl.descriptors import IntegerIntroductionMEALPY is the largest python library in the world for most of the cutting-edge meta-heuristic algorithms
(nature-inspired algorithms, black-box optimization, global search optimizers, iterative learning algorithms,
continuous optimization, derivative free optimization, gradient free optimization, zeroth order optimization,
stochastic search optimization, random search optimization). These algorithms belong to population-based algorithms
(PMA), which are the most popular algorithms in the field of approximate optimization.Free software:GNU General Public License (GPL) V3 licenseTotal algorithms: 215 (190 official (original, hybrid, variants), 25 developed)Documentation:https://mealpy.readthedocs.io/en/latest/Python versions:>=3.7xDependencies:numpy, scipy, pandas, matplotlibCitation RequestPlease include these citations if you plan to use this library:@article{van2023mealpy,
  title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
  author={Van Thieu, Nguyen and Mirjalili, Seyedali},
  journal={Journal of Systems Architecture},
  year={2023},
  publisher={Elsevier},
  doi={10.1016/j.sysarc.2023.102871}
}

@article{van2023groundwater,
  title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization},
  author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai},
  journal={Journal of Hydrology},
  volume={617},
  pages={129034},
  year={2023},
  publisher={Elsevier},
  doi={https://doi.org/10.1016/j.jhydrol.2022.129034}
}

@article{ahmed2021comprehensive,
  title={A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem},
  author={Ahmed, Ali Najah and Van Lam, To and Hung, Nguyen Duy and Van Thieu, Nguyen and Kisi, Ozgur and El-Shafie, Ahmed},
  journal={Applied Soft Computing},
  volume={105},
  pages={107282},
  year={2021},
  publisher={Elsevier},
  doi={10.1016/j.asoc.2021.107282}
}UsageGoalsOur goals are to implement all classical as well as the state-of-the-art nature-inspired algorithms, create a simple interface that helps researchers access optimization algorithms as quickly as possible, and share knowledge of the optimization field with everyone without a fee. What you can do with mealpy:Analyse parameters of meta-heuristic algorithms.Perform Qualitative and Quantitative Analysis of algorithms.Analyse rate of convergence of algorithms.Test and Analyse the scalability and the robustness of algorithms.Save results in various formats (csv, json, pickle, png, pdf, jpeg)Export and import models can also be done with Mealpy.Solve any optimization problemInstallationInstall the stable (latest) version fromPyPI release:$pipinstallmealpy==3.0.1Install the alpha/beta version from PyPi$pipinstallmealpy==2.5.4a6Install the pre-release version directly from the source code:$gitclonehttps://github.com/thieu1995/mealpy.git
$cdmealpy
$pythonsetup.pyinstallIn case, you want to install the development version from Github:$pipinstallgit+https://github.com/thieu1995/permetricsAfter installation, you can import Mealpy as any other Python module:$python
>>>importmealpy
>>>mealpy.__version__

>>>print(mealpy.get_all_optimizers())>>>model=mealpy.get_optimizer_by_name("OriginalWOA")(epoch=100,pop_size=50)ExamplesBefore dive into some examples, let me ask you a question. What type of problem are you trying to solve?
Additionally, what would be the solution for your specific problem?
Based on the table below, you can select an appropriate type of decision variables to use.ClassSyntaxProblem TypesFloatVarFloatVar(lb=(-10., )*7, ub=(10., )*7, name="delta")Continuous ProblemIntegerVarIntegerVar(lb=(-10., )*7, ub=(10., )*7, name="delta")LP, IP, NLP, QP, MIPStringVarStringVar(valid_sets=(("auto", "backward", "forward"), ("leaf", "branch", "root")), name="delta")ML, AI-optimizeBinaryVarBinaryVar(n_vars=11, name="delta")NetworksBoolVarBoolVar(n_vars=11, name="delta")ML, AI-optimizePermutationVarPermutationVar(valid_set=(-10, -4, 10, 6, -2), name="delta")Combinatorial OptimizationMixedSetVarMixedSetVar(valid_sets=(("auto", 2, 3, "backward", True), (0, "tournament", "round-robin")), name="delta")MIP,  MILPTransferBoolVarTransferBoolVar(n_vars=11, name="delta", tf_func="sstf_02")ML, AI-optimize, FeatureTransferBinaryVarTransferBinaryVar(n_vars=11, name="delta", tf_func="vstf_04")Networks, Feature SelectionLet's go through a basic and advanced example.Simple Benchmark FunctionUsing Problem dictfrommealpyimportFloatVar,SMAimportnumpyasnpdefobjective_function(solution):returnnp.sum(solution**2)problem={"obj_func":objective_function,"bounds":FloatVar(lb=(-100.,)*30,ub=(100.,)*30),"minmax":"min","log_to":None,}## Run the algorithmmodel=SMA.OriginalSMA(epoch=100,pop_size=50,pr=0.03)g_best=model.solve(problem)print(f"Best solution:{g_best.solution}, Best fitness:{g_best.target.fitness}")Define a custom Problem classPlease note that, there is no moregenerate_position,amend_solution, andfitness_functionin Problem class.We take care everything under the DataType Class above. Just choose which one fit for your problem.We recommend you define a custom class that inheritProblemclass if your decision variable is not FloatVarfrommealpyimportProblem,FloatVar,BBOimportnumpyasnp# Our custom problem classclassSquared(Problem):def__init__(self,bounds=None,minmax="min",name="Squared",data=None,**kwargs):self.name=nameself.data=datasuper().__init__(bounds,minmax,**kwargs)defobj_func(self,solution):x=self.decode_solution(solution)["my_var"]returnnp.sum(x**2)## Now, we define an algorithm, and pass an instance of our *Squared* class as the problem argument.bound=FloatVar(lb=(-10.,)*20,ub=(10.,)*20,name="my_var")problem=Squared(bounds=bound,minmax="min",name="Squared",data="Amazing")model=BBO.OriginalBBO(epoch=100,pop_size=20)g_best=model.solve(problem)The benefit of using custom Problem classfromsklearn.svmimportSVCfromsklearn.model_selectionimporttrain_test_splitfromsklearn.preprocessingimportStandardScalerfromsklearnimportdatasets,metricsfrommealpyimportFloatVar,StringVar,IntegerVar,BoolVar,MixedSetVar,SMA,Problem# Load the data set; In this example, the breast cancer dataset is loaded.X,y=datasets.load_breast_cancer(return_X_y=True)# Create training and test splitX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=1,stratify=y)sc=StandardScaler()X_train_std=sc.fit_transform(X_train)X_test_std=sc.transform(X_test)data={"X_train":X_train_std,"X_test":X_test_std,"y_train":y_train,"y_test":y_test}classSvmOptimizedProblem(Problem):def__init__(self,bounds=None,minmax="max",data=None,**kwargs):self.data=datasuper().__init__(bounds,minmax,**kwargs)defobj_func(self,x):x_decoded=self.decode_solution(x)C_paras,kernel_paras=x_decoded["C_paras"],x_decoded["kernel_paras"]degree,gamma,probability=x_decoded["degree_pras"],x_decoded["gamma_paras"],x_decoded["probability_paras"]svc=SVC(C=C_paras,kernel=kernel_paras,degree=degree,gamma=gamma,probability=probability,random_state=1)# Fit the modelsvc.fit(self.data["X_train"],self.data["y_train"])# Make the predictionsy_predict=svc.predict(self.data["X_test"])# Measure the performancereturnmetrics.accuracy_score(self.data["y_test"],y_predict)my_bounds=[FloatVar(lb=0.01,ub=1000.,name="C_paras"),StringVar(valid_sets=('linear','poly','rbf','sigmoid'),name="kernel_paras"),IntegerVar(lb=1,ub=5,name="degree_paras"),MixedSetVar(valid_sets=('scale','auto',0.01,0.05,0.1,0.5,1.0),name="gamma_paras"),BoolVar(n_vars=1,name="probability_paras"),]problem=SvmOptimizedProblem(bounds=my_bounds,minmax="max",data=data)model=SMA.OriginalSMA(epoch=100,pop_size=20)model.solve(problem)print(f"Best agent:{model.g_best}")print(f"Best solution:{model.g_best.solution}")print(f"Best accuracy:{model.g_best.target.fitness}")print(f"Best parameters:{model.problem.decode_solution(model.g_best.solution)}")Set Seed for Optimizer (So many people asking for this feature)You can set random seed number for each run of single optimizer.model=SMA.OriginalSMA(epoch=100,pop_size=50,pr=0.03)g_best=model.solve(problem=problem,seed=10)# Default seed=NoneLarge-Scale OptimizationfrommealpyimportFloatVar,SHADEimportnumpyasnpdefobjective_function(solution):returnnp.sum(solution**2)problem={"obj_func":objective_function,"bounds":FloatVar(lb=(-1000.,)*10000,ub=(1000.,)*10000),# 10000 dimensions"minmax":"min","log_to":"console",}## Run the algorithmoptimizer=SHADE.OriginalSHADE(epoch=10000,pop_size=100)g_best=optimizer.solve(problem)print(f"Best solution:{g_best.solution}, Best fitness:{g_best.target.fitness}")Distributed Optimization / Parallelization OptimizationPlease read the article titledMEALPY: An open-source library for latest meta-heuristic algorithms in Pythonto
gain a clear understanding of the concept of parallelization (distributed
optimization) in metaheuristics. Not all metaheuristics can be run in parallel.frommealpyimportFloatVar,SMAimportnumpyasnpdefobjective_function(solution):returnnp.sum(solution**2)problem={"obj_func":objective_function,"bounds":FloatVar(lb=(-100.,)*100,ub=(100.,)*100),"minmax":"min","log_to":"console",}## Run distributed SMA algorithm using 10 threadsoptimizer=SMA.OriginalSMA(epoch=10000,pop_size=100,pr=0.03)optimizer.solve(problem,mode="thread",n_workers=10)# Distributed to 10 threadsprint(f"Best solution:{optimizer.g_best.solution}, Best fitness:{optimizer.g_best.target.fitness}")## Run distributed SMA algorithm using 8 CPUs (cores)optimizer.solve(problem,mode="process",n_workers=8)# Distributed to 8 coresprint(f"Best solution:{optimizer.g_best.solution}, Best fitness:{optimizer.g_best.target.fitness}")Constrained Benchmark FunctionfrommealpyimportFloatVar,SMAimportnumpyasnp## Link: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119136507.app2defobjective_function(solution):defg1(x):return2*x[0]+2*x[1]+x[9]+x[10]-10defg2(x):return2*x[0]+2*x[2]+x[9]+x[10]-10defg3(x):return2*x[1]+2*x[2]+x[10]+x[11]-10defg4(x):return-8*x[0]+x[9]defg5(x):return-8*x[1]+x[10]defg6(x):return-8*x[2]+x[11]defg7(x):return-2*x[3]-x[4]+x[9]defg8(x):return-2*x[5]-x[6]+x[10]defg9(x):return-2*x[7]-x[8]+x[11]defviolate(value):return0ifvalue<=0elsevaluefx=5*np.sum(solution[:4])-5*np.sum(solution[:4]**2)-np.sum(solution[4:13])## Increase the punishment for g1 and g4 to boost the algorithm (You can choice any constraint instead of g1 and g4)fx+=violate(g1(solution))**2+violate(g2(solution))+violate(g3(solution))+\2*violate(g4(solution))+violate(g5(solution))+violate(g6(solution))+\violate(g7(solution))+violate(g8(solution))+violate(g9(solution))returnfxproblem={"obj_func":objective_function,"bounds":FloatVar(lb=[0,0,0,0,0,0,0,0,0,0,0,0,0],ub=[1,1,1,1,1,1,1,1,1,100,100,100,1]),"minmax":"min",}## Run the algorithmoptimizer=SMA.OriginalSMA(epoch=100,pop_size=50,pr=0.03)optimizer.solve(problem)print(f"Best solution:{optimizer.g_best.solution}, Best fitness:{optimizer.g_best.target.fitness}")Multi-objective Benchmark FunctionfrommealpyimportFloatVar,SMAimportnumpyasnp## Link: https://en.wikipedia.org/wiki/Test_functions_for_optimizationdefobjective_function(solution):defbooth(x,y):return(x+2*y-7)**2+(2*x+y-5)**2defbukin(x,y):return100*np.sqrt(np.abs(y-0.01*x**2))+0.01*np.abs(x+10)defmatyas(x,y):return0.26*(x**2+y**2)-0.48*x*yreturn[booth(solution[0],solution[1]),bukin(solution[0],solution[1]),matyas(solution[0],solution[1])]problem={"obj_func":objective_function,"bounds":FloatVar(lb=(-10,-10),ub=(10,10)),"minmax":"min","obj_weights":[0.4,0.1,0.5]# Define it or default value will be [1, 1, 1]}## Run the algorithmoptimizer=SMA.OriginalSMA(epoch=100,pop_size=50,pr=0.03)optimizer.solve(problem)print(f"Best solution:{optimizer.g_best.solution}, Best fitness:{optimizer.g_best.target.fitness}")## You can access all of available figures via object "history" like this:optimizer.history.save_global_objectives_chart(filename="hello/goc")optimizer.history.save_local_objectives_chart(filename="hello/loc")optimizer.history.save_global_best_fitness_chart(filename="hello/gbfc")optimizer.history.save_local_best_fitness_chart(filename="hello/lbfc")optimizer.history.save_runtime_chart(filename="hello/rtc")optimizer.history.save_exploration_exploitation_chart(filename="hello/eec")optimizer.history.save_diversity_chart(filename="hello/dc")optimizer.history.save_trajectory_chart(list_agent_idx=[3,5],selected_dimensions=[2],filename="hello/tc")Custom ProblemFor our custom problem, we can create a class and inherit from theProblemclass, named the child class the'Squared' class. In the initialization method of the 'Squared' class, we have to set thebounds, andminmaxof the problem (bounds: a problem's type, and minmax: a string specifying whether the problem is a 'min' or 'max' problem).Afterwards, we have to override the abstract methodobj_func(), which takes a parameter 'solution' (the solution
to be evaluated) and returns the function value. The resulting code should look something like the code snippet
below. 'Name' is an additional parameter we want to include in this class, and you can include any other additional
parameters you need. But remember to set up all additional parameters before super() called.frommealpyimportProblem,FloatVar,BBOimportnumpyasnp# Our custom problem classclassSquared(Problem):def__init__(self,bounds=None,minmax="min",name="Squared",data=None,**kwargs):self.name=nameself.data=datasuper().__init__(bounds,minmax,**kwargs)defobj_func(self,solution):returnnp.sum(solution**2)## Now, we define an algorithm, and pass an instance of our *Squared* class as the problem argument.problem=Squared(bounds=FloatVar(lb=(-10.,)*20,ub=(10.,)*20),minmax="min",name="Squared",data="Amazing")model=BBO.OriginalBBO(epoch=10,pop_size=50)g_best=model.solve(problem)## Show some attributesprint(g_best.solution)print(g_best.target.fitness)print(g_best.target.objectives)print(g_best)print(model.get_parameters())print(model.get_name())print(model.get_attributes()["g_best"])print(model.problem.get_name())print(model.problem.n_dims)print(model.problem.bounds)print(model.problem.lb)print(model.problem.ub)Tuner class (GridSearchCV/ParameterSearch, Hyper-parameter tuning)We build a dedicated class, Tuner, that can help you tune your algorithm's parameters.fromopfunu.cec_based.cec2017importF52017frommealpyimportFloatVar,BBO,Tuner## You can define your own problem, here I took the F5 benchmark function in CEC-2017 as an example.f1=F52017(30,f_bias=0)p1={"bounds":FloatVar(lb=f1.lb,ub=f1.ub),"obj_func":f1.evaluate,"minmax":"min","name":"F5","log_to":"console",}paras_bbo_grid={"epoch":[10,20,30,40],"pop_size":[50,100,150],"n_elites":[2,3,4,5],"p_m":[0.01,0.02,0.05]}term={"max_epoch":200,"max_time":20,"max_fe":10000}if__name__=="__main__":model=BBO.OriginalBBO()tuner=Tuner(model,paras_bbo_grid)tuner.execute(problem=p1,termination=term,n_trials=5,n_jobs=4,mode="thread",n_workers=4,verbose=True)## Solve this problem 5 times (n_trials) using 5 processes (n_jobs), each process will handle 1 trial.## The mode to run the solver is thread (mode), distributed to 4 threadsprint(tuner.best_row)print(tuner.best_score)print(tuner.best_params)print(type(tuner.best_params))print(tuner.best_algorithm)## Save results to csv filetuner.export_results(save_path="history",file_name="tuning_best_fit.csv")tuner.export_figures()## Re-solve the best model on your problemg_best=tuner.resolve(mode="thread",n_workers=4,termination=term)print(g_best.solution,g_best.target.fitness)print(tuner.algorithm.problem.get_name())print(tuner.best_algorithm.get_name())Multitask class (Multitask solver)We also build a dedicated class, Multitask, that can help you run several scenarios. For example:Run 1 algorithm with 1 problem, and multiple trialsRun 1 algorithm with multiple problems, and multiple trialsRun multiple algorithms with 1 problem, and multiple trialsRun multiple algorithms with multiple problems, and multiple trials#### Using multiple algorithm to solve multiple problems with multiple trials## Import librariesfromopfunu.cec_based.cec2017importF52017,F102017,F292017frommealpyimportFloatVarfrommealpyimportBBO,DEfrommealpyimportMultitask## Define your own problemsf1=F52017(30,f_bias=0)f2=F102017(30,f_bias=0)f3=F292017(30,f_bias=0)p1={"bounds":FloatVar(lb=f1.lb,ub=f1.ub),"obj_func":f1.evaluate,"minmax":"min","name":"F5","log_to":"console",}p2={"bounds":FloatVar(lb=f2.lb,ub=f2.ub),"obj_func":f2.evaluate,"minmax":"min","name":"F10","log_to":"console",}p3={"bounds":FloatVar(lb=f3.lb,ub=f3.ub),"obj_func":f3.evaluate,"minmax":"min","name":"F29","log_to":"console",}## Define modelsmodel1=BBO.DevBBO(epoch=10000,pop_size=50)model2=BBO.OriginalBBO(epoch=10000,pop_size=50)model3=DE.OriginalDE(epoch=10000,pop_size=50)model4=DE.SAP_DE(epoch=10000,pop_size=50)## Define termination if neededterm={"max_fe":3000}## Define and run Multitaskif__name__=="__main__":multitask=Multitask(algorithms=(model1,model2,model3,model4),problems=(p1,p2,p3),terminations=(term,),modes=("thread",),n_workers=4)# default modes = "single", default termination = epoch (as defined in problem dictionary)multitask.execute(n_trials=5,n_jobs=None,save_path="history",save_as="csv",save_convergence=True,verbose=False)# multitask.execute(n_trials=5, save_path="history", save_as="csv", save_convergence=True, verbose=False)## Check the directory: history/, you will see list of .csv result filesFor more usage examples please look atexamplesfolder.More advanced examples can also be found in theMealpy-examples repository.Get Visualize FiguresTutorialsMealpy ApplicationMealpy + Neural Network (Replace the Gradient Descent Optimizer)Time-series Problem:Traditional MLP
code:LinkHybrid code (Mealpy +
MLP):LinkClassification Problem:Traditional MLP
code:LinkHybrid code (Mealpy +
MLP):LinkMealpy + Neural Network (Optimize Neural Network Hyper-parameter)Code:LinkOther ApplicationsSolving Knapsack Problem (Discrete
problems):LinkSolving Product Planning Problem (Discrete
problems):LinkOptimize SVM (SVC)
model:LinkOptimize Linear Regression
Model:LinkTravelling Salesman Problem:https://github.com/thieu1995/MHA-TSPFeature selection problem:https://github.com/thieu1995/MHA-FSGet Visualize FiguresTutorialsTutorial VideosAll tutorial videos:LinkAll code examples:LinkAll visualization examples:LinkDocumentsOfficial Channels (questions, problems)Official source code repo:https://github.com/thieu1995/mealpyOfficial document:https://mealpy.readthedocs.io/Download releases:https://pypi.org/project/mealpy/Issue tracker:https://github.com/thieu1995/mealpy/issuesNotable changes log:https://github.com/thieu1995/mealpy/blob/master/ChangeLog.mdExamples with different meapy version:https://github.com/thieu1995/mealpy/blob/master/EXAMPLES.mdOfficial chat/support group:https://t.me/+fRVCJGuGJg1mNDg1This project also related to our another projects which are optimization and machine learning. Check it here:https://github.com/thieu1995/opfunuhttps://github.com/thieu1995/metaheuristicshttps://github.com/mafesehttps://github.com/permetricshttps://github.com/pfevaluatorhttps://github.com/MetaClusterhttps://github.com/enoppyhttps://github.com/aiir-teamMy CommentsMeta-heuristic Categories: (Based on this article:link)Evolutionary-based: Idea from Darwin's law of natural selection, evolutionary computingSwarm-based: Idea from movement, interaction of birds, organization of social ...Physics-based: Idea from physics law such as Newton's law of universal gravitation, black hole, multiverseHuman-based: Idea from human interaction such as queuing search, teaching learning, ...Biology-based: Idea from biology creature (or microorganism),...System-based: Idea from eco-system, immune-system, network-system, ...Math-based: Idea from mathematical form or mathematical law such as sin-cosinMusic-based: Idea from music instrumentDifficulty - Difficulty Level (Personal Opinion):Objective observation from author. Depend on the number of
parameters, number of equations, the original ideas, time spend for coding, source lines of code (SLOC).Easy: A few paras, few equations, SLOC very shortMedium: more equations than Easy level, SLOC longer than Easy levelHard: Lots of equations, SLOC longer than Medium level, the paper hard to read.Hard* - Very hard: Lots of equations, SLOC too long, the paper is very hard to read.** For newbie, we recommend to read the paper of algorithms which difficulty is "easy" or "medium" difficulty level.GroupNameModuleClassYearParasDifficultyEvolutionaryEvolutionary ProgrammingEPOriginalEP19643easyEvolutionary**LevyEP*3easyEvolutionaryEvolution StrategiesESOriginalES19713easyEvolutionary**LevyES*3easyEvolutionary**CMA_ES20032hardEvolutionary**Simple_CMA_ES20232mediumEvolutionaryMemetic AlgorithmMAOriginalMA19897easyEvolutionaryGenetic AlgorithmGABaseGA19924easyEvolutionary**SingleGA*7easyEvolutionary**MultiGA*7easyEvolutionary**EliteSingleGA*10easyEvolutionary**EliteMultiGA*10easyEvolutionaryDifferential EvolutionDEBaseDE19975easyEvolutionary**JADE20096mediumEvolutionary**SADE20052mediumEvolutionary**SAP_DE20063mediumEvolutionarySuccess-History Adaptation Differential EvolutionSHADEOriginalSHADE20134mediumEvolutionary**L_SHADE20144mediumEvolutionaryFlower Pollination AlgorithmFPAOriginalFPA20144mediumEvolutionaryCoral Reefs OptimizationCROOriginalCRO201411mediumEvolutionary**OCRO201912medium*********************SwarmParticle Swarm OptimizationPSOOriginalPSO19956easySwarm**PPSO20192mediumSwarm**HPSO_TVAC20174mediumSwarm**C_PSO20156mediumSwarm**CL_PSO20066mediumSwarmBacterial Foraging OptimizationBFOOriginalBFO200210hardSwarm**ABFO20198mediumSwarmBees AlgorithmBeesAOriginalBeesA20058mediumSwarm**ProbBeesA20155mediumSwarm**CleverBookBeesA20068mediumSwarmCat Swarm OptimizationCSOOriginalCSO200611hardSwarmArtificial Bee ColonyABCOriginalABC20078mediumSwarmAnt Colony OptimizationACOROriginalACOR20085easySwarmCuckoo Search AlgorithmCSAOriginalCSA20093mediumSwarmFirefly AlgorithmFFAOriginalFFA20098easySwarmFireworks AlgorithmFAOriginalFA20107mediumSwarmBat AlgorithmBAOriginalBA20106mediumSwarm**AdaptiveBA20108mediumSwarm**ModifiedBA*5mediumSwarmFruit-fly Optimization AlgorithmFOAOriginalFOA20122easySwarm**BaseFOA*2easySwarm**WhaleFOA20202mediumSwarmSocial Spider OptimizationSSpiderOOriginalSSpiderO20184hard*SwarmGrey Wolf OptimizerGWOOriginalGWO20142easySwarm**RW_GWO20192easySwarmSocial Spider AlgorithmSSpiderAOriginalSSpiderA20155mediumSwarmAnt Lion OptimizerALOOriginalALO20152easySwarm**BaseALO*2easySwarmMoth Flame OptimizationMFOOriginalMFO20152easySwarm**BaseMFO*2easySwarmElephant Herding OptimizationEHOOriginalEHO20155easySwarmJaya AlgorithmJAOriginalJA20162easySwarm**BaseJA*2easySwarm**LevyJA20212easySwarmWhale Optimization AlgorithmWOAOriginalWOA20162mediumSwarm**HI_WOA20193mediumSwarmDragonfly OptimizationDOOriginalDO20162mediumSwarmBird Swarm AlgorithmBSAOriginalBSA20169mediumSwarmSpotted Hyena OptimizerSHOOriginalSHO20174mediumSwarmSalp Swarm OptimizationSSOOriginalSSO20172easySwarmSwarm Robotics Search And RescueSRSROriginalSRSR20172hard*SwarmGrasshopper Optimisation AlgorithmGOAOriginalGOA20174easySwarmCoyote Optimization AlgorithmCOAOriginalCOA20183mediumSwarmMoth Search AlgorithmMSAOriginalMSA20185easySwarmSea Lion OptimizationSLOOriginalSLO20192mediumSwarm**ModifiedSLO*2mediumSwarm**ImprovedSLO20224mediumSwarmNake Mole*Rat AlgorithmNMRAOriginalNMRA20193easySwarm**ImprovedNMRA*4mediumSwarmPathfinder AlgorithmPFAOriginalPFA20192mediumSwarmSailfish OptimizerSFOOriginalSFO20195easySwarm**ImprovedSFO*3mediumSwarmHarris Hawks OptimizationHHOOriginalHHO20192mediumSwarmManta Ray Foraging OptimizationMRFOOriginalMRFO20203mediumSwarmBald Eagle SearchBESOriginalBES20207easySwarmSparrow Search AlgorithmSSAOriginalSSA20205mediumSwarm**BaseSSA*5mediumSwarmHunger Games SearchHGSOriginalHGS20214mediumSwarmAquila OptimizerAOOriginalAO20212easySwarmHybrid Grey Wolf * Whale Optimization AlgorithmGWOGWO_WOA20222easySwarmMarine Predators AlgorithmMPAOriginalMPA20202mediumSwarmHoney Badger AlgorithmHBAOriginalHBA20222easySwarmSand Cat Swarm OptimizationSCSOOriginalSCSO20222easySwarmTuna Swarm OptimizationTSOOriginalTSO20212mediumSwarmAfrican Vultures Optimization AlgorithmAVOAOriginalAVOA20227mediumSwarmArtificial Gorilla Troops OptimizationAGTOOriginalAGTO20215mediumSwarm**MGTO20233mediumSwarmArtificial Rabbits OptimizationAROOriginalARO20222easySwarm**LARO20222easySwarm**IARO20222easySwarmEgret Swarm Optimization AlgorithmESOAOriginalESOA20222mediumSwarmFox OptimizerFOXOriginalFOX20234easySwarmGolden Jackal OptimizationGJOOriginalGJO20222easySwarmGiant Trevally OptimizationGTOOriginalGTO20224mediumSwarm**Matlab101GTO20222mediumSwarm**Matlab102GTO20232hardSwarmMountain Gazelle OptimizerMGOOriginalMGO20222easySwarmSea-Horse OptimizationSeaHOOriginalSeaHO20222medium*********************PhysicsSimulated AnneallingSAOriginalSA19839mediumPhysics**GaussianSA*5mediumPhysics**SwarmSA19879mediumPhysicsWind Driven OptimizationWDOOriginalWDO20137easyPhysicsMulti*Verse OptimizerMVOOriginalMVO20164easyPhysics**BaseMVO*4easyPhysicsTug of War OptimizationTWOOriginalTWO20162easyPhysics**OppoTWO*2mediumPhysics**LevyTWO*2mediumPhysics**EnhancedTWO20202mediumPhysicsElectromagnetic Field OptimizationEFOOriginalEFO20166easyPhysics**BaseEFO*6mediumPhysicsNuclear Reaction OptimizationNROOriginalNRO20192hard*PhysicsHenry Gas Solubility OptimizationHGSOOriginalHGSO20193mediumPhysicsAtom Search OptimizationASOOriginalASO20194mediumPhysicsEquilibrium OptimizerEOOriginalEO20192easyPhysics**ModifiedEO20202mediumPhysics**AdaptiveEO20202mediumPhysicsArchimedes Optimization AlgorithmArchOAOriginalArchOA20218mediumPhysicsChernobyl Disaster OptimizationCDOOriginalCDO20232easyPhysicsEnergy Valley OptimizationEVOOriginalEVO20232mediumPhysicsFick's Law AlgorithmFLAOriginalFLA20238hardPhysicsPhysical Phenomenon of RIME-iceRIMEOriginalRIME20233easy*********************HumanCulture AlgorithmCAOriginalCA19943easyHumanImperialist Competitive AlgorithmICAOriginalICA20078hard*HumanTeaching Learning*based OptimizationTLOOriginalTLO20112easyHuman**BaseTLO20122easyHuman**ITLO20133mediumHumanBrain Storm OptimizationBSOOriginalBSO20118mediumHuman**ImprovedBSO20177mediumHumanQueuing Search AlgorithmQSAOriginalQSA20192hardHuman**BaseQSA*2hardHuman**OppoQSA*2hardHuman**LevyQSA*2hardHuman**ImprovedQSA20212hardHumanSearch And Rescue OptimizationSAROOriginalSARO20194mediumHuman**BaseSARO*4mediumHumanLife Choice*Based OptimizationLCOOriginalLCO20193easyHuman**BaseLCO*3easyHuman**ImprovedLCO*2easyHumanSocial Ski*Driver OptimizationSSDOOriginalSSDO20192easyHumanGaining Sharing Knowledge*based AlgorithmGSKAOriginalGSKA20196mediumHuman**BaseGSKA*4mediumHumanCoronavirus Herd Immunity OptimizationCHIOOriginalCHIO20204mediumHuman**BaseCHIO*4mediumHumanForensic*Based Investigation OptimizationFBIOOriginalFBIO20202mediumHuman**BaseFBIO*2mediumHumanBattle Royale OptimizationBROOriginalBRO20203mediumHuman**BaseBRO*3mediumHumanStudent Psychology Based OptimizationSPBOOriginalSPBO20202mediumHuman**DevSPBO*2mediumHumanHeap-based OptimizationHBOOriginalHBO20203mediumHumanHuman Conception OptimizationHCOOriginalHCO20226mediumHumanDwarf Mongoose Optimization AlgorithmDMOAOriginalDMOA20224mediumHuman**DevDMOA*3mediumHumanWar Strategy OptimizationWarSOOriginalWarSO20223easy*********************BioInvasive Weed OptimizationIWOOriginalIWO20067easyBioBiogeography*Based OptimizationBBOOriginalBBO20084easyBio**BaseBBO*4easyBioVirus Colony SearchVCSOriginalVCS20164hard*Bio**BaseVCS*4hard*BioSatin Bowerbird OptimizerSBOOriginalSBO20175easyBio**BaseSBO*5easyBioEarthworm Optimisation AlgorithmEOAOriginalEOA20188mediumBioWildebeest Herd OptimizationWHOOriginalWHO201912hardBioSlime Mould AlgorithmSMAOriginalSMA20203easyBio**BaseSMA*3easyBioBarnacles Mating OptimizerBMOOriginalBMO20183easyBioTunicate Swarm AlgorithmTSAOriginalTSA20202easyBioSymbiotic Organisms SearchSOSOriginalSOS20142mediumBioSeagull Optimization AlgorithmSOAOriginalSOA20193easyBio**DevSOA*3easyBioBrown-Bear Optimization AlgorithmBBOAOriginalBBOA20232mediumBioTree Physiology OptimizationTPOOriginalTPO20175medium*********************SystemGerminal Center OptimizationGCOOriginalGCO20184mediumSystem**BaseGCO*4mediumSystemWater Cycle AlgorithmWCAOriginalWCA20125mediumSystemArtificial Ecosystem*based OptimizationAEOOriginalAEO20192easySystem**EnhancedAEO20202mediumSystem**ModifiedAEO20202mediumSystem**ImprovedAEO20212mediumSystem**AugmentedAEO20222medium*********************MathHill ClimbingHCOriginalHC19933easyMath**SwarmHC*3easyMathCross-Entropy MethodCEMOriginalCEM19974easyMathTabu SearchTSOriginalTS20045easyMathSine Cosine AlgorithmSCAOriginalSCA20162easyMath**BaseSCA*2easyMath**QLE-SCA20224hardMathGradient-Based OptimizerGBOOriginalGBO20205mediumMathArithmetic Optimization AlgorithmAOAOrginalAOA20216easyMathChaos Game OptimizationCGOOriginalCGO20212easyMathPareto-like Sequential SamplingPSSOriginalPSS20214mediumMathweIghted meaN oF vectOrsINFOOriginalINFO20222mediumMathRUNge Kutta optimizerRUNOriginalRUN20212hardMathCircle Search AlgorithmCircleSAOriginalCircleSA20223easyMathSuccess History Intelligent OptimizationSHIOOriginalSHIO20222easy*********************MusicHarmony SearchHSOriginalHS20014easyMusic**BaseHS*4easy+++++++++++++++++++++WARNINGPLEASE CHECK PLAGIARISM BEFORE USING BELOW ALGORITHMS*****SwarmCoati Optimization AlgorithmCoatiOAOriginalCoatiOA20232easySwarmFennec For OptimizationFFOOriginalFFO20222easySwarmNorthern Goshawk OptimizationNGOOriginalNGO20212easySwarmOsprey Optimization AlgorithmOOAOriginalOOA20232easySwarmPelican Optimization AlgorithmPOAOriginalPOA20232easySwarmServal Optimization AlgorithmServalOAOriginalServalOA20222easySwarmSiberian Tiger OptimizationSTOOriginalSTO20222easySwarmTasmanian Devil OptimizationTDOOriginalTDO20222easySwarmWalrus Optimization AlgorithmWaOAOriginalWaOA20222easySwarmZebra Optimization AlgorithmZOAOriginalZOA20222easyHumanTeamwork Optimization AlgorithmTOAOriginalTOA20212easyReferencesAABC - Artificial Bee ColonyOriginalABC: Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.ACOR - Ant Colony Optimization.OriginalACOR: Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European journal of operational research, 185(3), 1155-1173.ALO - Ant Lion OptimizerOriginalALO: Mirjalili S (2015). “The Ant Lion Optimizer.” Advances in Engineering Software, 83, 80-98. doi:10.1016/j.advengsoft.2015.01.010BaseALO: The developed versionAEO - Artificial Ecosystem-based OptimizationOriginalAEO: Zhao, W., Wang, L., & Zhang, Z. (2019). Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 1-43.AugmentedAEO: Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2022). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 129034.ImprovedAEO: Rizk-Allah, R. M., & El-Fergany, A. A. (2020). Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model. International Journal of Hydrogen Energy.EnhancedAEO: Eid, A., Kamel, S., Korashy, A., & Khurshaid, T. (2020). An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations. IEEE Access, 8, 178493-178513.ModifiedAEO: Menesy, A. S., Sultan, H. M., Korashy, A., Banakhr, F. A., Ashmawy, M. G., & Kamel, S. (2020). Effective parameter extraction of different polymer electrolyte membrane fuel cell stack models using a modified artificial ecosystem optimization algorithm. IEEE Access, 8, 31892-31909.ASO - Atom Search OptimizationOriginalASO: Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283-304.ArchOA - Archimedes Optimization AlgorithmOriginalArchOA: Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3), 1531-1551.AOA - Arithmetic Optimization AlgorithmOriginalAOA: Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609.AO - Aquila OptimizerOriginalAO: Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering, 157, 107250.AVOA - African Vultures Optimization AlgorithmOriginalAVOA: Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408.AGTO - Artificial Gorilla Troops OptimizationOriginalAGTO: Abdollahzadeh, B., Soleimanian Gharehchopogh, F., & Mirjalili, S. (2021). Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 36(10), 5887-5958.ARO - Artificial Rabbits Optimization:OriginalARO: Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., & Zhao, W. (2022). Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082.BBFO - Bacterial Foraging OptimizationOriginalBFO: Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, 22(3), 52-67.ABFO: Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building resource auto-scaler with functional-link neural network and adaptive bacterial foraging optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham.BeesA - Bees AlgorithmOriginalBeesA: Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2005). The bees algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK.ProbBeesA: The probabilitic version of: Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd.BBO - Biogeography-Based OptimizationOriginalBBO: Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713.BaseBBO: The developed versionBA - Bat AlgorithmOriginalBA: Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg.AdaptiveBA: Wang, X., Wang, W. and Wang, Y., 2013, July. An adaptive bat algorithm. In International Conference on Intelligent Computing(pp. 216-223). Springer, Berlin, Heidelberg.ModifiedBA: Dong, H., Li, T., Ding, R. and Sun, J., 2018. A novel hybrid genetic algorithm with granular information for feature selection and optimization. Applied Soft Computing, 65, pp.33-46.BSO - Brain Storm OptimizationOriginalBSO: . Shi, Y. (2011, June). Brain storm optimization algorithm. In International conference in swarm intelligence (pp. 303-309). Springer, Berlin, Heidelberg.ImprovedBSO: El-Abd, M., 2017. Global-best brain storm optimization algorithm. Swarm and evolutionary computation, 37, pp.27-44.BSA - Bird Swarm AlgorithmOriginalBSA: Meng, X. B., Gao, X. Z., Lu, L., Liu, Y., & Zhang, H. (2016). A new bio-inspired optimisation algorithm:Bird Swarm Algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 28(4), 673-687.BMO - Barnacles Mating Optimizer:OriginalBMO: Sulaiman, M. H., Mustaffa, Z., Saari, M. M., Daniyal, H., Daud, M. R., Razali, S., & Mohamed, A. I. (2018, June). Barnacles mating optimizer: a bio-inspired algorithm for solving optimization problems. In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 265-270). IEEE.BES - Bald Eagle SearchOriginalBES: Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2019). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 1-28.BRO - Battle Royale OptimizationOriginalBRO: Rahkar Farshi, T. (2020). Battle royale optimization algorithm. Neural Computing and Applications, 1-19.BaseBRO: The developed versionCCA - Culture AlgorithmOriginalCA: Reynolds, R.G., 1994, February. An introduction to cultural algorithms. In Proceedings of the third annual conference on evolutionary programming (Vol. 24, pp. 131-139). River Edge, NJ: World Scientific.CEM - Cross Entropy MethodOriginalCEM: Rubinstein, R. (1999). The cross-entropy method for combinatorial and continuous optimization. Methodology and computing in applied probability, 1(2), 127-190.CSO - Cat Swarm OptimizationOriginalCSO: Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg.CSA - Cuckoo Search AlgorithmOriginalCSA: Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee.CRO - Coral Reefs OptimizationOriginalCRO: Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J. A. (2014). The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014.OCRO: Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient time-series forecasting using neural network and opposition-based coral reefs optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161.COA - Coyote Optimization AlgorithmOriginalCOA: Pierezan, J., & Coelho, L. D. S. (2018, July). Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE.CHIO - Coronavirus Herd Immunity OptimizationOriginalCHIO: Al-Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., & Abu Doush, I. (2021). Coronavirus herd immunity optimizer (CHIO). Neural Computing and Applications, 33(10), 5011-5042.BaseCHIO: The developed versionCGO - Chaos Game OptimizationOriginalCGO: Talatahari, S., & Azizi, M. (2021). Chaos Game Optimization: a novel metaheuristic algorithm. Artificial Intelligence Review, 54(2), 917-1004.CSA - Circle Search AlgorithmOriginalCSA: Qais, M. H., Hasanien, H. M., Turky, R. A., Alghuwainem, S., Tostado-Véliz, M., & Jurado, F. (2022). Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm. Mathematics, 10(10), 1626.DDE - Differential EvolutionBaseDE: Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.JADE: Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation, 13(5), 945-958.SADE: Qin, A. K., & Suganthan, P. N. (2005, September). Self-adaptive differential evolution algorithm for numerical optimization. In 2005 IEEE congress on evolutionary computation (Vol. 2, pp. 1785-1791). IEEE.SHADE: Tanabe, R., & Fukunaga, A. (2013, June). Success-history based parameter adaptation for differential evolution. In 2013 IEEE congress on evolutionary computation (pp. 71-78). IEEE.L_SHADE: Tanabe, R., & Fukunaga, A. S. (2014, July). Improving the search performance of SHADE using linear population size reduction. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 1658-1665). IEEE.SAP_DE: Teo, J. (2006). Exploring dynamic cls-adaptive populations in differential evolution. Soft Computing, 10(8), 673-686.DSA - Differential Search Algorithm (not done)BaseDSA: Civicioglu, P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 46, 229-247.DO - Dragonfly OptimizationOriginalDO: Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073.DMOA - Dwarf Mongoose Optimization AlgorithmOriginalDMOA: Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer methods in applied mechanics and engineering, 391, 114570.DevDMOA: The developed versionEES - Evolution Strategies.OriginalES: Schwefel, H. P. (1984). Evolution strategies: A family of non-linear optimization techniques based on imitating some principles of organic evolution. Annals of Operations Research, 1(2), 165-167.LevyES: Zhang, S., & Salari, E. (2005). Competitive learning vector quantization with evolution strategies for image compression. Optical Engineering, 44(2), 027006.EP - Evolutionary programming.OriginalEP: Fogel, L. J. (1994). Evolutionary programming in perspective: The top-down view. Computational intelligence: Imitating life.LevyEP: Lee, C.Y. and Yao, X., 2001, May. Evolutionary algorithms with adaptive lévy mutations. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546) (Vol. 1, pp. 568-575). IEEE.EHO - Elephant Herding Optimization.OriginalEHO: Wang, G. G., Deb, S., & Coelho, L. D. S. (2015, December). Elephant herding optimization. In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI) (pp. 1-5). IEEE.EFO - Electromagnetic Field Optimization.OriginalEFO:Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2016). Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 26, 8-22.BaseEFO: The developed versionEOA - Earthworm Optimisation Algorithm.OriginalEOA: Wang, G. G., Deb, S., & dos Santos Coelho, L. (2018). Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. IJBIC, 12(1), 1-22.EO - Equilibrium Optimizer.OriginalEO: Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2019). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems.ModifiedEO: Gupta, S., Deep, K., & Mirjalili, S. (2020). An efficient equilibrium optimizer with mutation strategy for numerical optimization. Applied Soft Computing, 96, 106542.AdaptiveEO: Wunnava, A., Naik, M. K., Panda, R., Jena, B., & Abraham, A. (2020). A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer. Engineering Applications of Artificial Intelligence, 94, 103836.FFFA - Firefly AlgorithmOriginalFFA: Łukasik, S., & Żak, S. (2009, October). Firefly algorithm for continuous constrained optimization tasks. In International conference on computational collective intelligence (pp. 97-106). Springer, Berlin, Heidelberg.FA - Fireworks algorithmOriginalFA: Tan, Y., & Zhu, Y. (2010, June). Fireworks algorithm for optimization. In International conference in swarm intelligence (pp. 355-364). Springer, Berlin, Heidelberg.FPA - Flower Pollination AlgorithmOriginalFPA: Yang, X. S. (2012, September). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Springer, Berlin, Heidelberg.FOA - Fruit-fly Optimization AlgorithmOriginalFOA: Pan, W. T. (2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74.BaseFOA: The developed versionWhaleFOA: Fan, Y., Wang, P., Heidari, A. A., Wang, M., Zhao, X., Chen, H., & Li, C. (2020). Boosted hunting-based fruit fly optimization and advances in real-world problems. Expert Systems with Applications, 159, 113502.FBIO - Forensic-Based Investigation OptimizationOriginalFBIO: Chou, J.S. and Nguyen, N.M., 2020. FBI inspired meta-optimization. Applied Soft Computing, p.106339.BaseFBIO: Fathy, A., Rezk, H. and Alanazi, T.M., 2021. Recent approach of forensic-based investigation algorithm for optimizing fractional order PID-based MPPT with proton exchange membrane fuel cell.IEEE Access,9, pp.18974-18992.FHO - Fire Hawk OptimizationOriginalFHO: Azizi, M., Talatahari, S., & Gandomi, A. H. (2022). Fire Hawk Optimizer: a novel metaheuristic algorithm. Artificial Intelligence Review, 1-77.GGA - Genetic AlgorithmBaseGA: Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.SingleGA: De Falco, I., Della Cioppa, A. and Tarantino, E., 2002. Mutation-based genetic algorithm: performance evaluation. Applied Soft Computing, 1(4), pp.285-299.MultiGA: De Jong, K.A. and Spears, W.M., 1992. A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of mathematics and Artificial intelligence, 5(1), pp.1-26.EliteSingleGA: Elite version of Single-point mutation GAEliteMultiGA: Elite version of Multiple-point mutation GAGWO - Grey Wolf OptimizerOriginalGWO: Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.RW_GWO: Gupta, S., & Deep, K. (2019). A novel random walk grey wolf optimizer. Swarm and evolutionary computation, 44, 101-112.GWO_WOA: Obadina, O. O., Thaha, M. A., Althoefer, K., & Shaheed, M. H. (2022). Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm. Journal of Vibration and Control, 28(15-16), 1992-2003.IGWO: Kaveh, A. & Zakian, P.. (2018). Improved GWO algorithm for optimal design of truss structures. Engineering with Computers. 34. 10.1007/s00366-017-0567-1.GOA - Grasshopper Optimisation AlgorithmOriginalGOA: Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30-47.GCO - Germinal Center OptimizationOriginalGCO: Villaseñor, C., Arana-Daniel, N., Alanis, A. Y., López-Franco, C., & Hernandez-Vargas, E. A. (2018). Germinal center optimization algorithm. International Journal of Computational Intelligence Systems, 12(1), 13-27.BaseGCO: The developed versionGSKA - Gaining Sharing Knowledge-based AlgorithmOriginalGSKA: Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2019). Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. International Journal of Machine Learning and Cybernetics, 1-29.BaseGSKA: Mohamed, A.W., Hadi, A.A., Mohamed, A.K. and Awad, N.H., 2020, July. Evaluating the performance of adaptive GainingSharing knowledge based algorithm on CEC 2020 benchmark problems. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.GBO - Gradient-Based OptimizerOriginalGBO: Ahmadianfar, I., Bozorg-Haddad, O., & Chu, X. (2020). Gradient-based optimizer: A new metaheuristic optimization algorithm. Information Sciences, 540, 131-159.HHC - Hill Climbing.OriginalHC: Talbi, E. G., & Muntean, T. (1993, January). Hill-climbing, simulated annealing and genetic algorithms: a comparative study and application to the mapping problem. In [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences (Vol. 2, pp. 565-573). IEEE.SwarmHC: The developed version based on swarm-based idea (Original is single-solution based method)HS - Harmony Search.OriginalHS: Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm:harmony search. simulation, 76(2), 60-68.BaseHS: The developed versionHHO - Harris Hawks Optimization.OriginalHHO: Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.HGSO - Henry Gas Solubility Optimization.OriginalHGSO: Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646-667.HGS - Hunger Games Search.OriginalHGS: Yang, Y., Chen, H., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search:Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864.HHOA - Horse Herd Optimization Algorithm (not done).BaseHHOA: MiarNaeimi, F., Azizyan, G., & Rashki, M. (2021). Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems. Knowledge-Based Systems, 213, 106711.HBA - Honey Badger Algorithm:OriginalHBA: Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192, 84-110.IIWO - Invasive Weed Optimization.OriginalIWO: Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355-366.ICA - Imperialist Competitive AlgorithmOriginalICA: Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Ieee.INFO - weIghted meaN oF vectOrs:OriginalINFO: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient     optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079.JJA - Jaya AlgorithmOriginalJA: Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34.BaseJA: The developed versionLevyJA: Iacca, G., dos Santos Junior, V. C., & de Melo, V. V. (2021). An improved Jaya optimization algorithm with Levy flight. Expert Systems with Applications, 165, 113902.KLLCO - Life Choice-based OptimizationOriginalLCO: Khatri, A., Gaba, A., Rana, K. P. S., & Kumar, V. (2019). A novel life choice-based optimizer. Soft Computing, 1-21.BaseLCO: The developed versionImprovedLCO: The improved version using Gaussian distribution and Mutation MechanismMMA - Memetic AlgorithmOriginalMA: Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989.MFO - Moth Flame OptimizationOriginalMFO: Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249.BaseMFO: The developed versionMVO - Multi-Verse OptimizerOriginalMVO: Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.BaseMVO: The developed versionMSA - Moth Search AlgorithmOriginalMSA: Wang, G. G. (2018). Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151-164.MRFO - Manta Ray Foraging OptimizationOriginalMRFO: Zhao, W., Zhang, Z., & Wang, L. (2020). Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 87, 103300.MPA - Marine Predators Algorithm:OriginalMPA: Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert systems with applications, 152, 113377.NNRO - Nuclear Reaction OptimizationOriginalNRO: Wei, Z., Huang, C., Wang, X., Han, T., & Li, Y. (2019). Nuclear Reaction Optimization: A novel and powerful physics-based algorithm for global optimization. IEEE Access.NMRA - Nake Mole-Rat AlgorithmOriginalNMRA: Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing and Applications, 31(12), 8837-8857.ImprovedNMRA: Singh, P., Mittal, N., Singh, U. and Salgotra, R., 2021. Naked mole-rat algorithm with improved exploration and exploitation capabilities to determine 2D and 3D coordinates of sensor nodes in WSNs. Arabian Journal for Science and Engineering, 46(2), pp.1155-1178.OPPSO - Particle Swarm OptimizationOriginalPSO: Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). Ieee.PPSO: Ghasemi, M., Akbari, E., Rahimnejad, A., Razavi, S. E., Ghavidel, S., & Li, L. (2019). Phasor particle swarm optimization: a simple and efficient variant of PSO. Soft Computing, 23(19), 9701-9718.HPSO_TVAC: Ghasemi, M., Aghaei, J., & Hadipour, M. (2017). New cls-organising hierarchical PSO with jumping time-varying acceleration coefficients. Electronics Letters, 53(20), 1360-1362.C_PSO: Liu, B., Wang, L., Jin, Y. H., Tang, F., & Huang, D. X. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 25(5), 1261-1271.CL_PSO: Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE transactions on evolutionary computation, 10(3), 281-295.PFA - Pathfinder AlgorithmOriginalPFA: Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545-568.PSS - Pareto-like Sequential SamplingOriginalPSS: Shaqfa, M., & Beyer, K. (2021). Pareto-like sequential sampling heuristic for global optimisation. Soft Computing, 25(14), 9077-9096.QQSA - Queuing Search AlgorithmOriginalQSA: Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464-490.BaseQSA: The developed versionOppoQSA: Zheng, X. and Nguyen, H., 2022. A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm. Chemosphere, 287, p.132251.LevyQSA: Abderazek, H., Hamza, F., Yildiz, A.R., Gao, L. and Sait, S.M., 2021. A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system. Materials Testing, 63(5), pp.442-447.ImprovedQSA: Nguyen, B.M., Hoang, B., Nguyen, T. and Nguyen, G., 2021. nQSV-Net: a novel queuing search variant for global space search and workload modeling. Journal of Ambient Intelligence and Humanized Computing, 12(1), pp.27-46.RRUN - RUNge Kutta optimizer:OriginalRUN: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079.SSA - Simulated AnneallingOriginalSA: Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.GaussianSA: Van Laarhoven, P. J., Aarts, E. H., van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing (pp. 7-15). Springer Netherlands.SwarmSA: My developed versionSSpiderO - Social Spider OptimizationOriginalSSpiderO: Cuevas, E., Cienfuegos, M., ZaldíVar, D., & Pérez-Cisneros, M. (2013). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications, 40(16), 6374-6384.SOS - Symbiotic Organisms Search:OriginalSOS: Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112.SSpiderA - Social Spider AlgorithmOriginalSSpiderA: James, J. Q., & Li, V. O. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614-627.SCA - Sine Cosine AlgorithmOriginalSCA: Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133.BaseSCA: Attia, A.F., El Sehiemy, R.A. and Hasanien, H.M., 2018. Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power & Energy Systems, 99, pp.331-343.SRSR - Swarm Robotics Search And RescueOriginalSRSR: Bakhshipour, M., Ghadi, M. J., & Namdari, F. (2017). Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach. Applied Soft Computing, 57, 708-726.SBO - Satin Bowerbird OptimizerOriginalSBO: Moosavi, S. H. S., & Bardsiri, V. K. (2017). Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1-15.BaseSBO: The developed versionSHO - Spotted Hyena OptimizerOriginalSHO: Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48-70.SSO - Salp Swarm OptimizationOriginalSSO: Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191.SFO - Sailfish OptimizerOriginalSFO: Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20-34.ImprovedSFO: Li, L.L., Shen, Q., Tseng, M.L. and Luo, S., 2021. Power system hybrid dynamic economic emission dispatch with wind energy based on improved sailfish algorithm. Journal of Cleaner Production, 316, p.128318.SARO - Search And Rescue OptimizationOriginalSARO: Shabani, A., Asgarian, B., Gharebaghi, S. A., Salido, M. A., & Giret, A. (2019). A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering, 2019.BaseSARO: The developed version using Levy-flightSSDO - Social Ski-Driver OptimizationOriginalSSDO: Tharwat, A., & Gabel, T. (2019). Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Computing and Applications, 1-14.SLO - Sea Lion OptimizationOriginalSLO: Masadeh, R., Mahafzah, B. A., & Sharieh, A. (2019). Sea Lion Optimization Algorithm. Sea, 10(5).ImprovedSLO: The developed versionModifiedSLO: Masadeh, R., Alsharman, N., Sharieh, A., Mahafzah, B.A. and Abdulrahman, A., 2021. Task scheduling on cloud computing based on sea lion optimization algorithm. International Journal of Web Information Systems.Seagull Optimization AlgorithmOriginalSOA: Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-based systems, 165, 169-196.DevSOA: The developed versionSMA - Slime Mould AlgorithmOriginalSMA: Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems.BaseSMA: The developed versionSSA - Sparrow Search AlgorithmOriginalSSA: Jiankai Xue & Bo Shen (2020) A novel swarm intelligence optimization approach: sparrow search algorithm, Systems Science & Control Engineering, 8:1, 22-34, DOI: 10.1080/21642583.2019.1708830BaseSSA: The developed versionSPBO - Student Psychology Based OptimizationOriginalSPBO: Das, B., Mukherjee, V., & Das, D. (2020). Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering software, 146, 102804.DevSPBO: The developed versionSCSO - Sand Cat Swarm OptimizationOriginalSCSO: Seyyedabbasi, A., & Kiani, F. (2022). Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 1-25.TTLO - Teaching Learning OptimizationOriginalTLO: Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.BaseTLO: Rao, R., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560.ImprovedTLO: Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710-720.TWO - Tug of War OptimizationOriginalTWO: Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492.OppoTWO: Kaveh, A., Almasi, P. and Khodagholi, A., 2022. Optimum Design of Castellated Beams Using Four Recently Developed Meta-heuristic Algorithms. Iranian Journal of Science and Technology, Transactions of Civil Engineering, pp.1-13.LevyTWO: The developed version using Levy-flightImprovedTWO: Nguyen, T., Hoang, B., Nguyen, G., & Nguyen, B. M. (2020). A new workload prediction model using extreme learning machine and enhanced tug of war optimization. Procedia Computer Science, 170, 362-369.TSA - Tunicate Swarm AlgorithmOriginalTSA: Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541.TSO - Tuna Swarm OptimizationOriginalTSO: Xie, L., Han, T., Zhou, H., Zhang, Z. R., Han, B., & Tang, A. (2021). Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization. Computational intelligence and Neuroscience, 2021.UVVCS - Virus Colony SearchOriginalVCS: Li, M. D., Zhao, H., Weng, X. W., & Han, T. (2016). A novel nature-inspired algorithm for optimization: Virus colony search. Advances in Engineering Software, 92, 65-88.BaseVCS: The developed versionWWCA - Water Cycle AlgorithmOriginalWCA: Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166.WOA - Whale Optimization AlgorithmOriginalWOA: Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.HI_WOA: Tang, C., Sun, W., Wu, W., & Xue, M. (2019, July). A hybrid improved whale optimization algorithm. In 2019 IEEE 15th International Conference on Control and Automation (ICCA) (pp. 362-367). IEEE.WHO - Wildebeest Herd OptimizationOriginalWHO: Amali, D., & Dinakaran, M. (2019). Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-14.WDO - Wind Driven OptimizationOriginalWDO: Bayraktar, Z., Komurcu, M., Bossard, J.A. and Werner, D.H., 2013. The wind driven optimization technique and its application in electromagnetics. IEEE transactions on antennas and propagation, 61(5), pp.2745-2757.XYZList of papers used MEALPYMin, J., Oh, M., Kim, W., Seo, H., & Paek, J. (2022, October). Evaluation of Metaheuristic Algorithms for TAS Scheduling in Time-Sensitive Networking. In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) (pp. 809-812). IEEE.Khozeimeh, F., Sharifrazi, D., Izadi, N. H., Joloudari, J. H., Shoeibi, A., Alizadehsani, R., ... & Islam, S. M. S. (2021). Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Scientific Reports, 11(1), 15343.Rajesh, K., Jain, E., & Kotecha, P. (2022). A Multi-Objective approach to the Electric Vehicle Routing Problem. arXiv preprint arXiv:2208.12440.Sánchez, A. J. H., & Upegui, F. R. (2022). Una herramienta para el diseño de redes MSMN de banda ancha en líneas de transmisión basada en algoritmos heurísticos de optimización comparados. Revista Ingeniería UC, 29(2), 106-123.Khanmohammadi, M., Armaghani, D. J., & Sabri Sabri, M. M. (2022). Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time. Mathematics, 10(19), 3563.Kudela, J. (2023). The Evolutionary Computation Methods No One Should Use. arXiv preprint arXiv:2301.01984.Vieira, M., Faia, R., Pinto, T., & Vale, Z. (2022, September). Schedule Peer-to-Peer Transactions of an Energy Community Using Particle Swarm. In 2022 18th International Conference on the European Energy Market (EEM) (pp. 1-6). IEEE.Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. Forecasting PM. MINING SCIENCE ANDTECHNOLOGY (Russia), 111.Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. (2022). Forecasting PM 2.5 emissions in open-pit minesusing a functional link neural network optimized by various optimization algorithms. Gornye nauki i tekhnologii= Mining Science and Technology (Russia), 7(2), 111-125.Doğan, E., & Yörükeren, N. (2022). Enhancement of Transmission System Security with Archimedes Optimization Algorithm.Ayub, N., Aurangzeb, K., Awais, M., & Ali, U. (2020, November). Electricity theft detection using CNN-GRU and manta ray foraging optimization algorithm. In 2020 IEEE 23Rd international multitopic conference (INMIC) (pp. 1-6). IEEE.Pintilie, L., Nechita, M. T., Suditu, G. D., Dafinescu, V., & Drăgoi, E. N. (2022). Photo-decolorization of Eriochrome Black T: process optimization with Differential Evolution algorithm. In PASEW-22, MESSH-22 & CABES-22 April 19–21, 2022 Paris (France). Eminent Association of Pioneers.LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. (2021). A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation, 67, 100973.Gottam, S., Nanda, S. J., & Maddila, R. K. (2021, December). A CNN-LSTM Model Trained with Grey Wolf Optimizer for Prediction of Household Power Consumption. In 2021 IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS) (pp. 355-360). IEEE.Darius, P. S., Devadason, J., & Solomon, D. G. (2022, December). Prospects of Ant Colony Optimization (ACO) in Various Domains. In 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C) (pp. 79-84). IEEE.Ayub, N., Irfan, M., Awais, M., Ali, U., Ali, T., Hamdi, M., ... & Muhammad, F. (2020). Big data analytics for short and medium-term electricity load forecasting using an AI techniques ensembler. Energies, 13(19), 5193.Biundini, I. Z., Melo, A. G., Coelho, F. O., Honório, L. M., Marcato, A. L., & Pinto, M. F. (2022). Experimentation and Simulation with Autonomous Coverage Path Planning for UAVs. Journal of Intelligent & Robotic Systems, 105(2), 46.Yousaf, I., Anwar, F., Imtiaz, S., Almadhor, A. S., Ishmanov, F., & Kim, S. W. (2022). An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System. Computational Intelligence and Neuroscience, 2022.Xu, L., Yan, W., & Ji, J. (2023). The research of a novel WOG-YOLO algorithm for autonomous driving object detection. Scientific reports, 13(1), 3699.Costache, R. D., Arabameri, A., Islam, A. R. M. T., Abba, S. I., Pandey, M., Ajin, R. S., & Pham, B. T. (2022). Flood susceptibility computation using state-of-the-art machine learning and optimization algorithms.Del Ser, J., Osaba, E., Martinez, A. D., Bilbao, M. N., Poyatos, J., Molina, D., & Herrera, F. (2021, December). More is not always better: insights from a massive comparison of meta-heuristic algorithms over real-parameter optimization problems. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE.Rustam, F., Aslam, N., De La Torre Díez, I., Khan, Y. D., Mazón, J. L. V., Rodríguez, C. L., & Ashraf, I. (2022, November). White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images. In Healthcare (Vol. 10, No. 11, p. 2230). MDPI.Neupane, D., Kafle, S., Gurung, S., Neupane, S., & Bhattarai, N. (2021). Optimal sizing and financial analysis of a stand-alone SPV-micro-hydropower hybrid system considering generation uncertainty. International Journal of Low-Carbon Technologies, 16(4), 1479-1491.Liang, R., Le-Hung, T., & Nguyen-Thoi, T. (2022). Energy consumption prediction of air-conditioning systems in eco-buildings using hunger games search optimization-based artificial neural network model. Journal of Building Engineering, 59, 105087.He, Z., Nguyen, H., Vu, T. H., Zhou, J., Asteris, P. G., & Mammou, A. (2022). Novel integrated approaches for predicting the compressibility of clay using cascade forward neural networks optimized by swarm-and evolution-based algorithms. Acta Geotechnica, 1-16.Xu, L., Yan, W., & Ji, J. (2022). The research of a novel WOG-YOLO algorithm forautonomous driving object detection.Nasir Ayub, M. I., Awais, M., Ali, U., Ali, T., Hamdi, M., Alghamdi, A., & Muhammad, F. Big Data Analytics for Short and Medium Term Electricity Load Forecasting using AI Techniques Ensembler.Xie, C., Nguyen, H., Choi, Y., & Armaghani, D. J. (2022). Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays. Geoscience Frontiers, 13(2), 101313.Hakemi, S., Houshmand, M., & Hosseini, S. A. (2022). A Dynamic Quantum-Inspired Genetic Algorithm with Lengthening Chromosome Size.Kashifi, M. T. City-Wide Crash Risk Prediction and Interpretation Using Deep Learning Model with Multi-Source Big Data. Available at SSRN 4329686.Nguyen, H., & Hoang, N. D. (2022). Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network. Automation in Construction, 140, 104371.Zheng, J., Lu, Z., Wu, K., Ning, G. H., & Li, D. (2020). Coinage-metal-based cyclic trinuclear complexes with metal–metal interactions: Theories to experiments and structures to functions. Chemical Reviews, 120(17), 9675-9742.Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2023). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 617, 129034.Mo, Z., Zhang, Z., Miao, Q., & Tsui, K. L. (2022). Intelligent Informative Frequency Band Searching Assisted by a Dynamic Bandit Tree Method for Machine Fault Diagnosis. IEEE/ASME Transactions on Mechatronics.Dangi, D., Chandel, S. T., Dixit, D. K., Sharma, S., & Bhagat, A. (2023). An Efficient Model for Sentiment Analysis using Artificial Rabbits Optimized Vector Functional Link Network. Expert Systems with Applications, 119849.Dey, S., Roychoudhury, R., Malakar, S., & Sarkar, R. (2022). An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images. Applied Soft Computing, 114, 108094.Mousavirad, S. J., & Alexandre, L. A. (2022). Population-based JPEG Image Compression: Problem Re-Formulation. arXiv preprint arXiv:2212.06313.Tsui, K. L. Intelligent Informative Frequency Band Searching Assisted by A Dynamic Bandit Tree Method for Machine Fault Diagnosis.Neupane, D. (2020). Optimal Sizing and Performance Analysis of Solar PV-Micro hydropower Hybrid System in the Context of Rural Area of Nepal (Doctoral dissertation, Pulchowk Campus).LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. Swarm and Evolutionary Computation.Vieira, M. A. (2022). Otimização dos custos operacionais de uma comunidade energética considerando transações locais em “peer-to-peer” (Doctoral dissertation).Toğaçar, M. (2022). Using DarkNet models and metaheuristic optimization methods together to detect weeds growing along with seedlings. Ecological Informatics, 68, 101519.Toğaçar, M. (2021). Detection of segmented uterine cancer images by Hotspot Detection method using deep learning models, Pigeon-Inspired Optimization, types-based dominant activation selection approaches. Computers in Biology and Medicine, 136, 104659.Khan, N. A Short Term Electricity Load and Price Forecasting Model Based on BAT Algorithm in Logistic Regression and CNN-GRU with WOA.Yelisetti, S., Saini, V. K., Kumar, R., & Lamba, R. (2022, May). Energy Consumption Cost Benefits through Smart Home Energy Management in Residential Buildings: An Indian Case Study. In 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET) (pp. 930-935). IEEE.Nguyen, H., Cao, M. T., Tran, X. L., Tran, T. H., & Hoang, N. D. (2022). A novel whale optimization algorithm optimized XGBoost regression for estimating bearing capacity of concrete piles. Neural Computing and Applications, 1-28.Hirsching, C., de Jongh, S., Eser, D., Suriyah, M., & Leibfried, T. (2022). Meta-heuristic optimization of control structure and design for MMC-HVdc applications. Electric Power Systems Research, 213, 108371.Amelin, V., Gatiyatullin, E., Romanov, N., Samarkhanov, R., Vasilyev, R., & Yanovich, Y. (2022). Black-Box for Blockchain Parameters Adjustment. IEEE Access, 10, 101795-101802.Ngo, T. Q., Nguyen, L. Q., & Tran, V. Q. (2022). Novel hybrid machine learning models including support vector machine with meta-heuristic algorithms in predicting unconfined compressive strength of organic soils stabilised with cement and lime. International Journal of Pavement Engineering, 1-18.Zhu, Y., & Iiduka, H. (2021). Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks. IEEE Access, 9, 143807-143823.Hakemi, S., Houshmand, M., KheirKhah, E., & Hosseini, S. A. (2022). A review of recent advances in quantum-inspired metaheuristics. Evolutionary Intelligence, 1-16.Das, A., Das, S. R., Panda, J. P., Dey, A., Gajrani, K. K., Somani, N., & Gupta, N. (2022). Machine learning based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool. arXiv preprint arXiv:2202.00596.Yelisetti, S., Saini, V. K., Kumar, R., Lamba, R., & Saxena, A. (2022). Optimal energy management system for residential buildings considering the time of use price with swarm intelligence algorithms. Journal of Building Engineering, 59, 105062.Valdés, G. T. (2022). Algoritmo para la detección de vehículos y peatones combinando CNN´ sy técnicas de búsqueda.Sallam, N. M., Saleh, A. I., Ali, H. A., & Abdelsalam, M. M. (2023). An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images. Alexandria Engineering Journal, 68, 39-66.