TY - JOUR ID - 22338 TI - A Non-dominated Sorting based Evolutionary Algorithm for Many-objective Optimization Problems JO - Scientia Iranica JA - SCI LA - en SN - 1026-3098 AU - Mane, S. U. AU - Narasinga Rao, M. R. AD - Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (Deemed to be University), Vaddeswaram, Guntur Dist., AP, India Y1 - 2021 PY - 2021 VL - 28 IS - 6 SP - 3293 EP - 3314 KW - Many-objective hybrid differential evolution algorithm KW - Non-dominated sorting KW - Decomposition-based approach KW - Differential Evolution Algorithm KW - Particle swarm optimization algorithm KW - Many-objective optimization problems DO - 10.24200/sci.2021.53026.3017 N2 - 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. UR - https://scientiairanica.sharif.edu/article_22338.html L1 - https://scientiairanica.sharif.edu/article_22338_33c6ac1720212393206cd1c273514e08.pdf ER -