Improved genetic algorithm based on particle swarm optimization-inspired reference point placement. Issue 7 (3rd July 2019)

• Record Type: Journal Article
• Authors: Essiet, Ima O.; Sun, Yanxia; Wang, Zenghui
• Is Part Of: Engineering optimization. Volume 51:Issue 7(2019)
• Page Start: 1097
• Page End: 1114
• Publication Date: 2019-07-03
• Subjects: Non-dominated sorting genetic algorithm -- reference points -- optimization -- inverted generational distance -- hyper-plane; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2018.1509961
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: This article investigates the use of optimal reference point placement to improve performance of non-dominated sorting genetic algorithm (NSGA). Placement of reference points for many-objective optimization is inspired by wheel and Von Neumann topologies of Particle Swarm Optimization (PSO). Results obtained show that the pattern of reference point placement determines performance efficiency of NSGA. The better-performing wheel topology (called wheel reference point genetic algorithm (wRPGA), is compared to three other many-objective evolutionary algorithms: knee-driven evolutionary algorithm (KnEA), non-dominated sorting genetic algorithm III (NSGAIII) and multi-objective evolutionary algorithm based on dominance and decomposition (MOEAD/D). The selected many-objective benchmark problems are Walking Fish Group 2 (WFG2) and Deb-Thiele-Laumanns-Zitzler 2 (DTLZ2). It is also tested on a 3-objective cost function for a hypothetical model of a stand-alone microgrid. Through the simulations, the wheel configuration performed 88.9% better than the Von Neumann configuration. The wheel topology also achieved better performance with respect to inverted generational distance (IGD) compared to KnEA, NSGAIII and MOEAD/D for 7 out of 15 IEEE Congress on Evolutionary Computation (CEC) 2017 benchmark problems. wRPGA gave a good approximation of the Pareto front for the 3-objective model representing the hypothetical microgrid.