Publication Type:

Journal Article

Source:

Soft Computing, Volume 18, Number 10, p.1949-1965 (2013)

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-84888768375&partnerID=40&md5=ad4403dea63835b9872b33b84d9a3070

Abstract:

This paper proposes a novel distributed differential evolution framework called distributed mixed variants (dynamic) differential evolution ( {Mathematical expression}. This novel framework is a heterogeneous mix of effective differential evolution (DE) and dynamic differential evolution (DDE) variants with diverse characteristics in a distributed framework to result in {Mathematical expression}. The {Mathematical expression}, discussed in this paper, constitute various proportions and combinations of DE/best/2/bin and DDE/best/2/bin as subpopulations with each variant evolving independently but also exchanging information amongst others to co-operatively enhance the efficacy of {Mathematical expression} as whole. The {Mathematical expression} variants have been run on 14 test problems of 30 dimensions to display their competitive performance over the distributed classical and dynamic versions of the constituent variants. The {Mathematical expression}, when benchmarked on a different 13 test problems of 500 as well as 1,000 dimensions, scaled well and outperformed, on an average, five existing distributed differential evolution algorithms.

Notes:

cited By (since 1996)0; Article in Press

Cite this Research Publication

G. Jeyakumar and Velayutham, C. S., “Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization”, Soft Computing, vol. 18, pp. 1949-1965, 2013.