Sequential Multi-objective Genetic Algorithm

Many real-world issues have multiple conflicting objectives, and optimization of the contradictory objectives is very difficult. In the recent years, the Multi-objective Evolutionary Algorithms (MOEAs) have shown a great performance in order to optimize such problems. Thus 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 such as the Sequential Multi-Objective Algorithm (SEQ-MOGA) have emerged to resolve the NSGAII problems. SEQ-MOGA presents a new survival selection that arranges the individuals systematically, and the chromosomes can cover the entire Pareto Front region. In this work, the Archive Sequential Multi-Objective Algorithm (ASMOGA) is proposed in order to develop and improve SEQ-MOGA. ASMOGA uses the archive technique in order to save the history of the search procedure so that the maintenance of the diversity in the decision space is adequately satisfied. In order to demonstrate the performance of ASMOGA, it is used and compared with several state-of-the-art MOEAs for optimizing the benchmark functions and designing the I-Beam problem. The optimization results are evaluated by the performance metrics such as the hyper-volume, generational distance, spacing, and t-test (a statistical test). Based on the results obtained, the superiority of the proposed algorithm is clearly identified.