Title: Heterogeneous mixing of dynamic differential evolution variants in distributed frame work for global optimisation problems

Authors: G. Jeyakumar; C. Shunmuga Velaytham

Addresses: Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India ' Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

Abstract: Differential evolution (DE) is a real parameter optimisation algorithm added to the pool of algorithms under evolutionary computing field. DE is well known for simplicity and robustness. The dynamic differential evolution (DDE) was proposed in the literature as an extension to DE, to alleviate the static population update mechanism of DE. Since the island-based distributed models are the natural extension of DE to parallelise it with structured population, they can also be extended for DDE. This paper, initially, implements distributed versions for 14 variants of DDE and also proposes an algorithm heterogeneous mixing of dynamic differential evolution variants (hmDDEv) to mix different DDE variants in island-based distributed model. The proposed hmDDEv algorithm is implemented and validated against a well-defined benchmarking suite with 14 benchmarking functions, by comparing it with its constituent DDE variants. The efficacy of hmDDEv is also validated with two state-of-the-art distributed DE algorithms.

Keywords: dynamic differential evolution; DDE; island models; distributed algorithm; mixed variants; intelligence; population migration; heterogeneous mixing; structured population; population update mechanism.

DOI: 10.1504/IJAIP.2022.124316

International Journal of Advanced Intelligence Paradigms, 2022 Vol.22 No.3/4, pp.318 - 335

Received: 23 Dec 2016
Accepted: 04 Oct 2017
Published online: 22 Jul 2022 *

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