Query: optimization algorithm

title: A comprehensive survey: Whale Optimization Algorithm and its applications
abstract: Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta heuristics for purposes of decision making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.

title: A Systematic and Meta Analysis Survey of Whale Optimization Algorithm
abstract: The whale optimization algorithm (WOA) is a nature inspired metaheuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This algorithm has shown its ability to solve many problems. Comprehensive surveys have been conducted about some other nature inspired algorithms, such as ABC and PSO. Nonetheless, no survey search work has been conducted on WOA. Therefore, in this paper, a systematic and meta analysis survey of WOA is conducted to help researchers to use it in different areas or hybridize it with other common algorithms. Thus, WOA is presented in depth in terms of algorithmic backgrounds, its characteristics, limitations, modifications, hybridizations, and applications. Next, WOA performances are presented to solve different problems. Then, the statistical results of WOA modifications and hybridizations are established and compared with the most common optimization algorithms and WOA. The survey's results indicate that WOA performs better than other common algorithms in terms of convergence speed and balancing between exploration and exploitation. WOA modifications and hybridizations also perform well compared to WOA. In addition, our investigation paves a way to present a new technique by hybridizing both WOA and BAT algorithms. The BAT algorithm is used for the exploration phase, whereas the WOA algorithm is used for the exploitation phase. Finally, statistical results obtained from WOA BAT are very competitive and better than WOA in 16 benchmarks functions. WOA BAT also outperforms well in 13 functions from CEC2005 and 7 functions from CEC2019.

title: Moth flame optimization algorithm: variants and applications
abstract: This paper thoroughly presents a comprehensive review of the so called moth flame optimization (MFO) and analyzes its main characteristics. MFO is considered one of the promising metaheuristic algorithms and successfully applied in various optimization problems in a wide range of fields, such as power and energy systems, economic dispatch, engineering design, image processing and medical applications. This manuscript describes the available literature on MFO, including its variants and hybridization, the growth of MFO publications, MFO application areas, theoretical analysis and comparisons of MFO with other algorithms. Conclusions focus on the current work on MFO, highlight its weaknesses, and suggest possible future research directions. Researchers and practitioners of MFO belonging to different fields, like the domains of optimization, medical, engineering, clustering and data mining, among others will benefit from this study.

title: Particle swarm optimization algorithm: an overview
abstract: Particle swarm optimization (PSO) is a population based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.

title: Grasshopper Optimization Algorithm: Theory, Literature Review, and Application in Hand Posture Estimation
abstract: This chapter covers the fundamental concepts of the recently proposed Grasshopper Optimization Algorithm (GOA) The inspiration, mathematical model, and the algorithm are presented in details. A brief literature review of this algorithm including different variants, improvement, hybrids, and applications are given too. The performance of GOA is tested on a set of test functions including unimodal, multi modal, and composite. The results show the ability of GOA in improving the quality of a random population, transiting from exploration to exploitation, showing high coverage of the search space, and accelerating the convergence curve over the course of iterations. The chapter also applies the GOA algorithm to a challenging problem in the field of hand posture estimation. It is observed that GOA finds an accurate configuration for a 3D hand model to match a given hand image acquired from a camera.

title: Recent Studies on Chicken Swarm Optimization algorithm: a review (2014 2018)
abstract: Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient based optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social networks. In recent years population based bio inspired algorithms have demonstrated competitive performance on a wide range of optimization problems. Chicken Swarm Optimization Algorithm (CSO) is one of such bio inspired meta heuristic algorithms mimicking the behaviour of chicken swarm. It is reported in many literature that CSO outperforms a number of well known meta heuristics in a wide range of benchmark problems. This paper presents a review of various issues related to CSO like general biology, fundamentals, variants of CSO, performance of CSO, and applications of CSO.

title: Whale Optimization Algorithm: Theory, Literature Review, and Application in Designing Photonic Crystal Filters
abstract: This chapter presents and analyzes the Whale Optimization Algorithm. The inspiration of this algorithm is first discussed in details, which is the bubble net foraging behaviour of humpback whales in nature. The mathematical models of this algorithm is then discussed. Due to the large number of applications, a brief literature review of WOA is provided including recent works on the algorithms itself and its applications. The chapter also tests the performance of WOA on several test functions and a real case study in the field of photonic crystal filter. The qualitative and quantitative results show that merits of this algorithm for solving a wide range of challenging problems.

title: Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm
abstract: Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end users over high speed computer networks (the Internet) Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state of the art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.

title: Improved whale optimization algorithm for feature selection in Arabic sentiment analysis
abstract: To help individuals or companies make a systematic and more accurate decisions, sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. However, WOA suffers from the same problem faced by many other optimization algorithms and tend to fall in local optima. To overcome these problems, two improvements for WOA algorithm are proposed in this paper. The first improvement includes using Elite Opposition Based Learning (EOBL) at initialization phase of WOA. The second improvement involves the incorporation of evolutionary operators from Differential Evolution algorithm at the end of each WOA iteration including mutation, crossover, and selection operators. In addition, we also used Information Gain (IG) as a filter features selection technique with WOA using Support Vector Machine (SVM) classifier to reduce the search space explored by WOA. To verify our proposed approach, four Arabic benchmark datasets for sentiment analysis are used since there are only a few studies in sentiment analysis conducted for Arabic language as compared to English. The proposed algorithm is compared with six well known optimization algorithms and two deep learning algorithms. The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.

title: Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm
abstract: Feature subset selection is a process to choose a set of relevant features from a high dimensionality dataset to improve the performance of classifiers. The meaningful words extracted from data forms a set of features for sentiment analysis. Many evolutionary algorithms, like the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been applied to feature subset selection problem and computational performance can still be improved. This research presents a solution to feature subset selection problem for classification of sentiments using ensemble based classifiers. It consists of a hybrid technique of minimum redundancy and maximum relevance (mRMR) and Forest Optimization Algorithm (FOA) based feature selection. Ensemble based classification is implemented to optimize the results of individual classifiers. The Forest Optimization Algorithm as a feature selection technique has been applied to various classification datasets from the UCI machine learning repository. The classifiers used for ensemble methods for UCI repository datasets are the k Nearest Neighbor (k NN) and Naive Bayes (NB) For the classification of sentiments, 15 20% improvement has been recorded. The dataset used for classification of sentiments is Blitzer's dataset consisting of reviews of electronic products. The results are further improved by ensemble of k NN, NB, and Support Vector Machine (SVM) with an accuracy of 95% for the classification of sentiment tasks.