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Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization

Publication Type : Journal Article

Publisher : Soft Computing

Source : Soft Computing – Springer, Volume 18, Number 10, p.1949-1965 (2014)

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

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2013

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.

Cite this Research Publication : Dr. Jeyakumar G. and Dr. Shunmuga Velayutham C., “Distributed heterogeneous mixing of differential and dynamic differential evolution variants for unconstrained global optimization”, Soft Computing – Springer, vol. 18, pp. 1949-1965, 2014.

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