An efficient Differential Evolution based algorithm for solving multi-objective optimization problems

In the present study, a modified variant of Differential Evolution (DE) algorithm for solving multi-objective optimization problems is presented. The proposed algorithm, named Multi-Objective Differential Evolution Algorithm (MODEA) utilizes the advantages of Opposition-Based Learning for generating an initial population of potential candidates and the concept of random localization in mutation step. Finally, it introduces a new selection mechanism for generating a well distributed Pareto optimal front. The performance of proposed algorithm is investigated on a set of nine bi-objective and five tri-objective benchmark test functions and the results are compared with some recently modified versions of DE for MOPs and some other Multi Objective Evolutionary Algorithms (MOEAs). The empirical analysis of the numerical results shows the efficiency of the proposed algorithm.

► An enhanced DE variant (MODEA) is proposed for solving multi-objective optimization problems (MOPs). ► It is a fusion of opposition based learning, random localization and a single population DE structure. ► MODEA introduces a new selection mechanism for generating a well distributed Pareto optimal front. ► The efficiency of MODEA is validated on a set of 9 bi-objective and 5 tri-objective problems. ► Modifications/enhancements embedded in DE provides an efficient mechanism for solving MOPs.