Handling multiple objectives using k‐means clustering guided multiobjective evolutionary algorithm

Abstract

AbstractMultiobjective optimization problems (MOPs) are very popular these days and have gained continuous research attention. These problems involve a minimum of two conflicting objectives that need to be optimized simultaneously. Multiobjective evolutionary algorithms (MOEAs) optimize these objectives by targeting solutions on the Pareto front. To judge the performance of different MOEAs, the convergence rate and population diversity are used. In order to create a good algorithm with a high convergence rate and diversity, the k‐means clustering algorithm is combined with a real parameter version of the improved environmental adaptation method (IEAM‐RP) in this paper. This is done as IEAM‐RP converges very fast and the k‐means clustering algorithm is used to provide diversity in decision search space. To check the performance of the proposed algorithm, it has been compared with nine state‐of‐the‐art multiobjective algorithms (MOPSOtridist, MOEA/D‐STM, MOEA/D, MOPSO, NSGA‐II, MOPSOses, MOGOA, MODA, MOALO) using standard multiobjective benchmark functions. Further, the performance of the proposed algorithm is compared against IBEA and SMS‐EMOA using walking fish group (WFG) test suit. The experimental results prove the effectiveness of the proposed algorithm.