An enhanced chimp optimization algorithm for continuous optimization domains

Chimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fields, researchers have higher requirements for native algorithms. In this paper, an...

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Bibliographic Details
Published in:Complex & intelligent systems Vol. 8; no. 1; pp. 65 - 82
Main Authors: Jia, Heming, Sun, Kangjian, Zhang, Wanying, Leng, Xin
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.02.2022
Springer Nature B.V
Subjects:
Algorithms
Antennae
Benchmarks
Complexity
Computational Intelligence
Correlation coefficients
Data Structures and Information Theory
Design engineering
Engineering
Heuristic methods
Mathematical analysis
Multilayer perceptrons
Mutation
Operators (mathematics)
Optimization
Optimization algorithms
Original Article
Polynomials
Statistical analysis
Spearman’s rank correlation coefficient
Chimp optimization algorithm
Highly disruptive polynomial mutation
Beetle antennae operator
Continuous optimization domains
ISSN:2199-4536, 2198-6053
Online Access:Get full text
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Summary:Chimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp optimization algorithm (EChOA) is proposed to improve the accuracy of solutions. First, the highly disruptive polynomial mutation is used to initialize the population, which provides the foundation for global search. Next, Spearman’s rank correlation coefficient of the chimps with the lowest social status is calculated with respect to the leader chimp. To reduce the probability of falling into the local optimum, the beetle antennae operator is used to improve the less fit chimps while gaining visual capability. Three strategies enhance the exploration and exploitation of the native algorithm. To verify the function optimization performance, EChOA is comprehensively analyzed on 12 classical benchmark functions and 15 CEC2017 benchmark functions. Besides, the practicability of EChOA is also highlighted by three engineering design problems and training multilayer perceptron. Compared with ChOA and five state-of-the-art algorithms, the statistical results show that EChOA has strong competitive capabilities and promising prospects.
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-021-00346-5