Adaptive chaotic equilibrium optimiser. (27th May 2022)

• Record Type: Journal Article
• Authors: Yang, Lin; Li, Jiayi; Xiong, Runqun; Todo, Yuki; Gao, Shangce
• Is Part Of: International journal of bio-inspired computation. Volume 19:Number 3(2022)
• Page Start: 147
• Page End: 157
• Publication Date: 2022-05-27
• Subjects: chaotic local search -- CLS -- chaotic maps -- equilibrium optimiser -- memetic algorithm; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

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.