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.