a non-dominated sorting based evolutionary algorithm for many-objective optimization problems

The optimization problems with more than three objectives are many-objective optimization problems exist in various scientific and engineering domains. the existing multi-objective evolutionary algorithmic approaches primarily developed to address problems up to three objectives. such multi-objective evolutionary algorithms do not found effective to address the many-objective optimization problems. the limitations of existing multi-objective evolutionary algorithms initiated the need to develop a specific algorithm which efficiently solves the many-objective optimization problems. the proposed work presents the design of the maohde to address maops. initially, ns-mode ns-mopso algorithms developed by incorporating the non-dominated sorting approach from nsga-ii, the ranking approach, weight vector, and reference points. the widely used tchebycheff – a decomposition-based approach applied to decompose the maops. the maohde algorithm developed by hybridizing the ns-mode with ns- mopso. the presented approach’s strength is revealed using 20 instances of dtlz functions. the effectiveness and efficiency are verified by comparing with maojaya, rd-emo, nsga-iii, moea/d, moea/dd, rvea, and moea/d-m2m algorithms. from the results, it is observed that the hybridization of ns-mode and ns-mopso as maohde responds better than its competitors for most of the test instances or it is competitive. the convergence rate is also good as compared with other state-of-art algorithms.