## 1 Introduction
### The Literature Review  
Authors of [11] exhibit a mathematical model of the mud ring feeding methodology, and its advantages over other current optimization methods are tested using two case studies. A data-driven surrogate-assisted technique is presented in [12] for dealing with high-dimensional large-scale MACEED challenges. This work, outlined in reference [13], presents a hybrid dynamic economic environmental dispatch model that combines an energy storage device, wind turbines, and solar systems, along with thermal power units. The aim of this model is to stabilize the production of renewable energy sources. The authors in [14] propose an advanced algorithm (the knee-guided algorithm (KGA))to simply addresses the EED problem. In this algorithm, the solution is determined using the lowest Manhattan distance technique, which defines the optimal solution. The suggested algorithm aims on finding a solution near the knee point, improving convergence, and delivering the knee solution rather than the entire POF. This approach increases the accessibility of the algorithm's results to policymakers in thermal power plants. Study [15] suggests a hybrid optimization solution for the electrical power systems' multi-objective economic emission dispatch (MOEED) issue. In study [16], researchers provide and execute an adapted iteration of the modified marine predators algorithm (MMPA) to tackle both single- and multi-objective CEED problems. It is recommended that MMPA be used to enhance the efficiency of regular MPA. To forestall the untimely aggregation of knowledge, it incorporates a complete learning strategy in which the best practices of all participants are shared. For reliability-based dynamic economic emission dispatch (DEEDR), the authors of [17] have developed an enhanced NSGA-III (I-NSGA-III). I-NSGA-III incorporates a distinctive crossover operator by using an angle-based connection and normal distribution approach. The authors of article [18] propose a distributed optimization strategy on a hybrid microgrid system to decrease the expenses associated with power generation. The authors of study [19] propose a nondominated sorting genetic algorithm III with three crossover strategies (NSGA-III-TCS) to address the challenges related to combined heat and power dynamic economic emission dispatch (CHPDEED), both with and without forbidden operation zones. In study [20], to handle DEED-PEV, the authors suggest a new NSGA-II (NNSGA-II). In NNSGA-II, the simulated binary crossover operator is swapped out for a Gaussian-based one, and the crossover frequency of each individual is adaptively adjusted depending on its position in the population. In paper [21], the researcher combines demand side management (DSM) and multi-objective dynamic economic and emission dispatch (MODEED) to examine the advantages of DSM on the generation side to minimize economic and emission dispatch problems separately and simultaneously with and without DSM. In paper [22], the competitive swarm optimization (CSO) algorithm, a newly discovered evolutionary method, is used to find the optimal solution for several objectives, including minimizing production costs, carbon emissions, voltage fluctuations, and power losses. The CSO algorithm's efficiency is measured against that of numerous cutting-edge metaheuristics, including GA, PSO, CSA, ABC, and SHADE-SF. The economic emission dispatch problems for combined heat and power production are tackled in article [23] by proposing an improved version of the bare-bone multi-objective particle swarm optimization (IBBMOPSO). In research paper [24], a primal-dual technique is used to replace the bi-level profit-maximizing model with linear single-level optimization. To address an environmental economic dispatch challenge, the authors of article [25] suggest an ordered optimization outline for the MMEG arrangement, which would allow for the energy coordination strategy to be produced in a decentralized fashion.