Title: Adaptive chaotic equilibrium optimiser

Authors: Lin Yang; Jiayi Li; Runqun Xiong; Yuki Todo; Shangce Gao

Addresses: Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan ' Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan ' School of Computer Science and Engineering, Southeast University, Nanjing, 211189, China ' Faculty of Electrical, Information and Communication Engineering, Kanazawa University, Ishikawa, 9201192, Japan ' Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan

Abstract: Equilibrium optimiser (EO) is a new algorithm inspired by the control volume mass balance model. It uses particles with concentration as the search agents to search for the optimal solution. The equilibrium pool is an important part for EO to update particles. To improve the quality of the equilibrium pool, a scheme based on differential radius and adaptive chaotic local search is proposed. The resultant algorithm is termed as adaptive chaotic equilibrium optimiser (CEO). CEO improves the population diversity of the original algorithm and obtains a better balance between exploration and exploitation. The performance of CEO is verified based on a number of benchmark functions with 30 and 50 dimensions taken from IEEE CEC2017. In addition, four real-world optimisation problems are employed to further validate the effectiveness of CEO.

Keywords: chaotic local search; CLS; chaotic maps; equilibrium optimiser; memetic algorithm.

DOI: 10.1504/IJBIC.2022.123125

International Journal of Bio-Inspired Computation, 2022 Vol.19 No.3, pp.147 - 157

Received: 20 May 2021
Accepted: 18 Aug 2021
Published online: 30 May 2022 *

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