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Differential Evolution with Alternating Strategies: A Novel Algorithm for Numeric Function Optimization

Mohammad Shafiul Alam, Md. Tawseef Alam, Farniba Khan, A. A. Fattah Islam, Md. Rasel Kabir. Published in Algorithms.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Mohammad Shafiul Alam, Md. Tawseef Alam, Farniba Khan, A. A. Fattah Islam, Md. Rasel Kabir
10.5120/cae2016652030

Mohammad Shafiul Alam, Md. Tawseef Alam, Farniba Khan, Fattah A A Islam and Md. Rasel Kabir. Article: Differential Evolution with Alternating Strategies: A Novel Algorithm for Numeric Function Optimization. Communications on Applied Electronics 4(2):12-16, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Mohammad Shafiul Alam and Md. Tawseef Alam and Farniba Khan and A. A. Fattah Islam and Md. Rasel Kabir},
	title = {Article: Differential Evolution with Alternating Strategies: A Novel Algorithm for Numeric Function Optimization},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {2},
	pages = {12-16},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

The Differential Evolution (DE) is a prominent meta-heuristic algorithm that has been successfully employed to numerous complex and diverse problems from the fields of mathematics, science and engineering. DE belongs to the evolutionary family of algorithms which is based on the Darwinian theory of natural selection and evolution. DE maintains a population of candidate solutions and uses the vector differences between randomly picked candidate solution vectors to produce new, improved solutions to advance its evolutionary optimization process, generation by generation. This paper introduces a novel DE-variant — the DE with Alternating Strategies (DEAS) and evaluates its performance using a number of benchmark problems on numeric function optimization. DEAS effectively combines the exploitative and explorative characteristics of five different DEvariants by randomly alternating and executing these DEvariants in a single algorithm. The experimental results indicate that DE-AS can perform better than many other existing DE-variants on most of the benchmark functions, in terms of both final solution quality and convergence speed.

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Keywords

Evolutionary algorithm, differential evolution, exploitation and exploration, numeric function optimization.