A new probe guided mutation operator for more efficient exploration of the search space: an experimental analysis

K. Liagkouras and K. Metaxiotis

International Journal of Operational Research, 2016, vol. 25, issue 2, 212-251

Abstract: This paper re-examines the classical polynomial mutation (PLM) operator and proposes a probe guided version of the PLM for more efficient exploration of the search space. The proposed probe guided mutation (PGM) operator applied to two well-known MOEAs, namely the non-dominated sorting genetic algorithm II (NSGAII) and strength Pareto evolutionary algorithm 2 (SPEA2), under two different sets of test functions. The relevant results are compared with the results derived by the same MOEAs by using their typical configuration with the PLM operator. The experimental results show that the proposed probe guided mutation operator outperforms the classical polynomial mutation operator, based on a number of different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it.

Keywords: multi-objective optimisation; evolutionary algorithms; mutation operators; search space; polynomial mutation; PLM operator; probe guided mutation operator; non-dominated sorting genetic algorithms; NSGA-II; Pareto front. (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:25:y:2016:i:2:p:212-251

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