Heterogeneous mixing of dynamic differential evolution variants in distributed frame work for global optimisation problems. (11th July 2022)

• Record Type: Journal Article
• Authors: Jeyakumar, G.; Velaytham, C. Shunmuga
• Is Part Of: International journal of advanced intelligence paradigms. Volume 22:Number 3/4(2022)
• Page Start: 318
• Page End: 335
• Publication Date: 2022-07-11
• Subjects: dynamic differential evolution -- DDE -- island models -- distributed algorithm -- mixed variants -- intelligence -- population migration -- heterogeneous mixing -- structured population -- population update mechanism; Artificial intelligence -- Periodicals; Machine theory -- Periodicals; Fuzzy logic -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-0386

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.