A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems

Yuting Liu; Hongwei Ding; Zongshan Wang; Gushen Jin; Bo Li; Zhijun Yang; Gaurav Dhiman; Yuting Liu; Hongwei Ding; Zongshan Wang; Gushen Jin; Bo Li; Zhijun Yang; Gaurav Dhiman

doi:10.3934/mbe.2023768

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 17242-17271. doi: 10.3934/mbe.2023768

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A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems

1.
School of Information Science and Engineering, Yunnan University, Kunming, China
2.
Glasgow College, University of Electronic Science and Technology of China, Chengdu, China
3.
Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
4.
University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Mohali, India
5.
Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India

Academic Editor: Jorge Bernardino

Received: 12 June 2023 Revised: 19 July 2023 Accepted: 02 August 2023 Published: 04 September 2023

The equilibrium optimizer (EO) algorithm is a newly developed physics-based optimization algorithm, which inspired by a mixed dynamic mass balance equation on a controlled fixed volume. The EO algorithm has a number of strengths, such as simple structure, easy implementation, few parameters and its effectiveness has been demonstrated on numerical optimization problems. However, the canonical EO still presents some drawbacks, such as poor balance between exploration and exploitation operation, tendency to get stuck in local optima and low convergence accuracy. To tackle these limitations, this paper proposes a new EO-based approach with an adaptive gbest-guided search mechanism and a chaos mechanism (called a chaos-based adaptive equilibrium optimizer algorithm (ACEO)). Firstly, an adaptive gbest-guided mechanism is injected to enrich the population diversity and expand the search range. Next, the chaos mechanism is incorporated to enable the algorithm to escape from the local optima. The effectiveness of the developed ACEO is demonstrated on 23 classical benchmark functions, and compared with the canonical EO, EO variants and other frontier metaheuristic approaches. The experimental results reveal that the developed ACEO method remarkably outperforms the canonical EO and other competitors. In addition, ACEO is implemented to solve a mobile robot path planning (MRPP) task, and compared with other typical metaheuristic techniques. The comparison indicates that ACEO beats its competitors, and the ACEO algorithm can provide high-quality feasible solutions for MRPP.
- metaheuristic algorithms,
- equilibrium optimizer,
- chaos,
- global optimization,
- robot path planning
Citation: Yuting Liu, Hongwei Ding, Zongshan Wang, Gushen Jin, Bo Li, Zhijun Yang, Gaurav Dhiman. A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 17242-17271. doi: 10.3934/mbe.2023768

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Abstract

The equilibrium optimizer (EO) algorithm is a newly developed physics-based optimization algorithm, which inspired by a mixed dynamic mass balance equation on a controlled fixed volume. The EO algorithm has a number of strengths, such as simple structure, easy implementation, few parameters and its effectiveness has been demonstrated on numerical optimization problems. However, the canonical EO still presents some drawbacks, such as poor balance between exploration and exploitation operation, tendency to get stuck in local optima and low convergence accuracy. To tackle these limitations, this paper proposes a new EO-based approach with an adaptive gbest-guided search mechanism and a chaos mechanism (called a chaos-based adaptive equilibrium optimizer algorithm (ACEO)). Firstly, an adaptive gbest-guided mechanism is injected to enrich the population diversity and expand the search range. Next, the chaos mechanism is incorporated to enable the algorithm to escape from the local optima. The effectiveness of the developed ACEO is demonstrated on 23 classical benchmark functions, and compared with the canonical EO, EO variants and other frontier metaheuristic approaches. The experimental results reveal that the developed ACEO method remarkably outperforms the canonical EO and other competitors. In addition, ACEO is implemented to solve a mobile robot path planning (MRPP) task, and compared with other typical metaheuristic techniques. The comparison indicates that ACEO beats its competitors, and the ACEO algorithm can provide high-quality feasible solutions for MRPP.

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