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

Authors: K. Liagkouras; K. Metaxiotis

Addresses: Decision Support Systems Laboratory, Department of Informatics, University of Piraeus, 80, Karaoli and Dimitriou Str., 18534 Piraeus, Greece ' Decision Support Systems Laboratory, Department of Informatics, University of Piraeus, 80, Karaoli and Dimitriou Str., 18534 Piraeus, Greece

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.

DOI: 10.1504/IJOR.2016.073957

International Journal of Operational Research, 2016 Vol.25 No.2, pp.212 - 251

Received: 06 Nov 2013
Accepted: 24 Jan 2014
Published online: 31 Dec 2015 *

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