TY  - EJOU
AU  - Wang, Jun 
AU  - Zhang, Linxi 
AU  - Zhang, Hao 
AU  - Peng, Funan 
AU  - El-Meligy, Mohammed A. 
AU  - Sharaf, Mohamed 
AU  - Fu, Qiang 

TI  - Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 79
IS  - 1
SN  - 1546-2226

AB  - The existing algorithms for solving multi-objective optimization problems fall into three main categories: Decomposition-based, dominance-based, and indicator-based. Traditional multi-objective optimization problems mainly focus on objectives, treating decision variables as a total variable to solve the problem without considering the critical role of decision variables in objective optimization. As seen, a variety of decision variable grouping algorithms have been proposed. However, these algorithms are relatively broad for the changes of most decision variables in the evolution process and are time-consuming in the process of finding the Pareto frontier. To solve these problems, a multi-objective optimization algorithm for grouping decision variables based on extreme point Pareto frontier (MOEA-DV/EPF) is proposed. This algorithm adopts a preprocessing rule to solve the Pareto optimal solution set of extreme points generated by simultaneous evolution in various target directions, obtains the basic Pareto front surface to determine the convergence effect, and analyzes the convergence and distribution effects of decision variables. In the later stages of algorithm optimization, different mutation strategies are adopted according to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals, thus enhancing the performance of the algorithm. Evaluation validation of the test functions shows that this algorithm can solve the multi-objective optimization problem more efficiently.
KW  - Multi-objective evolutionary optimization algorithm; decision variables grouping; extreme point; pareto frontier

DO  - 10.32604/cmc.2024.048495