A fast multi-objective optimization using an efficient ideal gas molecular movement algorithm

Recently, the ideal gas molecular movement (IGMM) algorithm was proposed by the authors as a new metaheuristic optimization technique for solving SOPs. In this paper, the intention is to extend the IGMM to solve MOPs while some modifications to the algorithm are taken place. The major improvement to the algorithm comprises usage of a neighbor-based non-dominated selection technique and defining a set of non-dominated solutions stored in an archive causing a globally faster convergence of the procedure. To evaluate the proposed algorithm, a set of standard benchmark problems, the so-called ZDT functions and two engineering benchmarks, are solved and the results were compared with five known multi-objective algorithms provided in the literature. Three different performance metrics; generational distance, spacing and maximum spread are introduced as well to evaluate multi-objective optimization problems. The Wilcoxon's rank-sum nonparametric statistical test was also attempted which resulted on the fact that the proposed algorithm may exhibit a significantly better performance than those other techniques. The results from the real engineering applications also prove the advancement of the MO-IGMM performance in practice. Compared to five other multi-objective optimization evolutionary algorithms, simulation results show that in most cases, the proposed MO-IGMM is capable to find a much better uniformly spread of solutions with a faster convergence to the true Pareto optimal front.