Article: Empirical investigations on evolution strategies to self-adapt the mutation and crossover parameters of differential evolution algorithm Journal: International Journal of Intelligent Systems Technologies and Applications (IJISTA) 2021 Vol.20 No.2 pp.103 - 125 Abstract: Differential evolution (DE) is a simple and robust evolutionary algorithm. Incorporation of parameter control mechanisms to DE is one thriving research area to improve its performance. Evolving the parameters along with the candidate solutions is a popular parameter control strategy. This paper proposes to investigate different parameter evolution strategies for two of its control parameters - mutation step size (F) and crossover probability (CR), using a well-defined set of four benchmarking problems with diverse characteristics and two performance metrics. This study discusses the influence of F and CR on the performance of DE, implements 25 difference instances of self-adaptive strategy for F and CR, analyses the performance to select the best strategy and validates the superiority of the proposed strategy on solving the RFID reader placement problem. The study found that the proposed strategy could solve the benchmarking functions and the chosen real-world problem faster than classical DE algorithm. Inderscience Publishers - linking academia, business and industry through research

Title: Empirical investigations on evolution strategies to self-adapt the mutation and crossover parameters of differential evolution algorithm

Authors: Dhanya M. Dhanalakshmy; G. Jeyakumar; C. Shunmuga Velayutham

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

Abstract: Differential evolution (DE) is a simple and robust evolutionary algorithm. Incorporation of parameter control mechanisms to DE is one thriving research area to improve its performance. Evolving the parameters along with the candidate solutions is a popular parameter control strategy. This paper proposes to investigate different parameter evolution strategies for two of its control parameters - mutation step size (F) and crossover probability (CR), using a well-defined set of four benchmarking problems with diverse characteristics and two performance metrics. This study discusses the influence of F and CR on the performance of DE, implements 25 difference instances of self-adaptive strategy for F and CR, analyses the performance to select the best strategy and validates the superiority of the proposed strategy on solving the RFID reader placement problem. The study found that the proposed strategy could solve the benchmarking functions and the chosen real-world problem faster than classical DE algorithm.

Keywords: differential evolution; self-adaptation; scale factor; crossover rate; evolving parameters.

DOI: 10.1504/IJISTA.2021.119028

International Journal of Intelligent Systems Technologies and Applications, 2021 Vol.20 No.2, pp.103 - 125

Received: 09 May 2020
Accepted: 19 Oct 2020
Published online: 18 Nov 2021 *

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Title: Empirical investigations on evolution strategies to self-adapt the mutation and crossover parameters of differential evolution algorithm

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