Title: Selection strategies for gravitational constant G in artificial physics optimisation based on analysis of convergence properties

Authors: Liping Xie; Jianchao Zeng; Richard A. Formato

Addresses: Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, No. 66 Waliu Road, Wanbailin District, Taiyuan, Shanxi, 030024, China ' Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, No. 66 Waliu Road, Wanbailin District, Taiyuan, Shanxi, 030024, China ' P.O. Box 1714, Harwich, MA 02645 USA

Abstract: The gravitational constant G is a particularly important parameter in artificial physics optimisation (APO) because it influences the algorithm's convergence. APO is a population-based heuristic whose swarm at each step can be divided into two distinct subsets: a divergent subset, and a convergent subset, the former containing all individuals exhibiting divergent behaviour, and the latter all others exhibiting convergent behaviour. How APO's population is apportioned between the divergent and convergent subsets is largely determined by the value of G. Two strategies for assigning its value were studied: a constant G, and an adaptive G. The disadvantage of the constant G case is mitigated by adaptive G by tuning the swarm's distribution between the two subsets. These strategies for selecting G were tested against several benchmark functions, and the results show that APO with an adaptive G outperforms APO with a constant G.

Keywords: proof of convergence; artificial physics optimisation; APO; physicomimetics; global optimisation; gravitational force; virtual force; Newton's law; convergence properties; gravitational constant G; bio-inspired computation.

DOI: 10.1504/IJBIC.2012.051412

International Journal of Bio-Inspired Computation, 2012 Vol.4 No.6, pp.380 - 391

Received: 18 Sep 2012
Accepted: 25 Oct 2012

Published online: 22 Sep 2014 *

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