sequential multi-objective genetic algorithm

Many of the real-world issues have multiple conflicting objectives that the optimization between contradictory objectives is very difficult. in recent years, the multi-objective evolutionary algorithms (moeas) have shown great performance to optimize such problems. so, the development of moeas will always lead to the advancement of science. the non-dominated sorting genetic algorithm ii (nsgaii) is considered as one of the most used evolutionary algorithms, and many moeas have emerged to resolve nsgaii problems, such as the sequential multi-objective algorithm (seq-moga). seq-moga presents a new survival selection that arranges individuals systematically, and the chromosomes can cover the entire pareto front region. in this study, the archive sequential multi-objective algorithm (asmoga) is proposed to develop and improve seq-moga. asmoga uses the archive technique to save the history of the search procedure, so that the maintenance of the diversity in the decision space is satisfied adequately. to demonstrate the performance of asmoga, it is used and compared with several state-of-the-art moeas for optimizing benchmark functions and designing the i-beam problem. the optimization results are evaluated by performance metrics such as hypervolume, generational distance, spacing, and the t-test (a statistical test); based on the results, the superiority of the proposed algorithm is identified clearly.