A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems

Metaheuristics in recent years has proven its effectiveness; however, robust algorithms that can solve real-world problems are always needed. In this paper, we suggest the first extended version of recently introduced gaining–sharing knowledge optimization (GSK) algorithm, named multiobjective (MOGSK), to deal with (MOPs). MOGSK employs an external archive population store nondominated solutions generated thus far, aim guiding during exploration process. Furthermore, fast sorting crowding distance was incorporated sustain diversity and ensure convergence towards Pareto optimal set, while ϵ-dominance relation used update solutions. helps provide a good boost diversity, coverage, overall. The validation proposed conducted using five biobjective (ZDT) seven three-objective test functions (DTLZ) problems, along CEC 2021, fifty-five total, including power electronics, process design synthesis, mechanical design, chemical engineering, system optimization. compared existing algorithms, MOEAD, eMOEA, MOPSO, NSGAII, SPEA2, KnEA, GrEA. experimental findings show behavior our against comparative particular problems.