Adaptive chimp optimization algorithm with chaotic map for global numerical optimization problems

Chimp optimization algorithm (ChOA) is a meta-heuristic algorithm inspired by individual intelligence and sexual motivation during group hunting. It is designed to speed up the convergence of the optimal solution. Because of its simplicity and low computational cost, the algorithm has been widely us...

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Bibliographic Details
Published in:The Journal of supercomputing Vol. 79; no. 6; pp. 6507 - 6537
Main Authors: Wang, Yiwen, Liu, Hao, Ding, Guiyan, Tu, Liangping
Format: Journal Article
Language:English
Published: New York Springer US 01.04.2023
Springer Nature B.V
Subjects:
Accuracy
Adaptive algorithms
Chaos theory
Compilers
Computer Science
Convergence
Exploitation
Global optimization
Heuristic methods
Interpreters
Mathematical models
Optimization algorithms
Processor Architectures
Programming Languages
Adaptive parameter
Chimp optimization algorithm
Tent chaotic map
Lévy flight
ISSN:0920-8542, 1573-0484
Online Access:Get full text
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Summary:Chimp optimization algorithm (ChOA) is a meta-heuristic algorithm inspired by individual intelligence and sexual motivation during group hunting. It is designed to speed up the convergence of the optimal solution. Because of its simplicity and low computational cost, the algorithm has been widely used to solve complex global optimization problem. But in the process of searching, it is easy to fall into the local optima, and the balance between exploitation and exploration cannot be realized well. In this paper, an adaptive chimp optimization algorithm called AChOA is proposed. Firstly, the Tent chaotic map is firstly used to initialize the chimp population to obtain a better initial solutions and improve convergence precision. Secondly, adaptive non linear convergence factor and adaptive weight are introduced in the global search stage, and the parameters vary adaptively according to the number of iterations and population diversity, so as to improve the population diversity. Thirdly, the Lévy flight strategy is introduced into the position update formula to mitigate the shortcomings of ChOA algorithm, such as finding the local optima rather than the global optima, and lack of balance between the exploitation and exploration process. Finally, a comparison with 10 famous algorithms on 19 benchmark functions of the solving accuracy and convergence speed of AChOA is presented. The results show that AChOA has the advantages of fast convergence speed, high solution accuracy.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-022-04886-6