A Cooperative Co-evolutionary Approach for Multi-objective Optimization

Abstract

In today’s world, the interest on multi-objective optimization through evolutionary algorithms (EAs) is growing day by day. However, most of the relative researches are confined within small scale with relatively fewer number of decision variables limited within 30, though real-world multi-objective optimization problems deal with, most of the times, with more than hundred decision variables. Also, optimization with fully separable decision variables along with non-separable decision variables leads to more optimal solutions than dealing with only any one of the both. In this paper, we have proposed an algorithm, which deals with medium- to large-scale multi-objective decision variables, compares the optimal solutions of separable and non-separable decision variables, and accepts the one having most optimized decision. Here we have adopted the test functions (large-scale multiobjective and many-objective optimization test problems for separable decision variables and Zitzler–Deb–Thiele test suit for non-separable decision variables) that are scalable with more than 100 decision variables and can range the results of both separable and non-separable decision variables.