A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems-Reference-Cited by-同舟云学术

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

Published:2023 Issue:9 Volume:20 Page:17242-17271
ISSN:1551-0018
Container-title:Mathematical Biosciences and Engineering
language:
Short-container-title:MBE

Author:

Liu Yuting¹,Ding Hongwei¹,Wang Zongshan¹,Jin Gushen²,Li Bo¹,Yang Zhijun¹,Dhiman Gaurav³⁴⁵

Affiliation:

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

Abstract

<abstract> <p>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.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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