An Opposition-based Self-adaptive Hybridized Differential Evolution Algorithm for Multi-objective Optimization (OSADE)

This paper presents a novel opposition-based self-adaptive hybridized Differential Evolution algorithm termed as OSADE for solving continuous multi-objective optimization problems. OSADE is developed using a modified version of a self-adaptive Differential Evolution variant and hybridizing it with the Multi-objective Evolutionary Gradient Search (MO-EGS) to act as a form of local search. Through the use of a test suite of benchmark problems, a comparative study of this newly developed algorithm and some state-of-the-art algorithms, such as NSGA-II, Non-dominated Sorting Differential Evolution (NSDE), MOEA/D-SBX, MOEA/D-DE and MO-EGS, is being presented by employing the Inverted Generational Distance (IGD) and the Hausdorff Distance (HD) performance indicators. From the simulation results, it is seen that OSADE is able to achieve competitive, if not better, performance when compared to the other algorithms in this study.