A Mating Selection Based on Modified Strengthened Dominance Relation for NSGA-III

Saykat Dutta, Sri Srinivasa Raju M, Rammohan Mallipeddi, Kedar Nath Das and Dong-Gyu Lee
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Saykat Dutta: Department of Mathematics, National Institute of Technology Silchar, Assam 788010, India
Sri Srinivasa Raju M: Department of Mathematics, National Institute of Technology Silchar, Assam 788010, India
Rammohan Mallipeddi: Department of Artificial Intelligence, School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea
Kedar Nath Das: Department of Mathematics, National Institute of Technology Silchar, Assam 788010, India
Dong-Gyu Lee: Department of Artificial Intelligence, School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea

Mathematics, 2021, vol. 9, issue 22, 1-22

Abstract: In multi/many-objective evolutionary algorithms (MOEAs), to alleviate the degraded convergence pressure of Pareto dominance with the increase in the number of objectives, numerous modified dominance relationships were proposed. Recently, the strengthened dominance relation (SDR) has been proposed, where the dominance area of a solution is determined by convergence degree and niche size ( ? ¯ ). Later, in controlled SDR (CSDR), ? ¯ and an additional parameter ( k ) associated with the convergence degree are dynamically adjusted depending on the iteration count. Depending on the problem characteristics and the distribution of the current population, different situations require different values of k , rendering the linear reduction of k based on the generation count ineffective. This is because a particular value of k is expected to bias the dominance relationship towards a particular region on the Pareto front (PF). In addition, due to the same reason, using SDR or CSDR in the environmental selection cannot preserve the diversity of solutions required to cover the entire PF. Therefore, we propose an MOEA, referred to as NSGA-III*, where (1) a modified SDR (MSDR)-based mating selection with an adaptive ensemble of parameter k would prioritize parents from specific sections of the PF depending on k , and (2) the traditional weight vector and non-dominated sorting-based environmental selection of NSGA-III would protect the solutions corresponding to the entire PF. The performance of NSGA-III* is favourably compared with state-of-the-art MOEAs on DTLZ and WFG test suites with up to 10 objectives.

Keywords: convergence; decomposition; diversity; dominance; ensemble (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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