Citation: Wu, H.; Zhang, F.; Gao, T.
Improved Chimpanzee Search
Algorithm with Multi-Strategy
Fusion and Its Application. Machines
2023, 11, 250. https://doi.org/
10.3390/machines11020250
Academic Editor: Dan Zhang
Received: 14 December 2022
Revised: 30 January 2023
Accepted: 6 February 2023
Published: 8 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
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4.0/).
Article
Improved Chimpanzee Search Algorithm with Multi-Strategy
Fusion and Its Application
Hongda Wu, Fuxing Zhang and Teng Gao *
School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034, China
* Correspondence: gaoteng@dlpu.edu.cn
Abstract:
An improved chimpanzee optimization algorithm incorporating multiple strategies (IM-
SChoA) is proposed to address the problems of initialized population boundary aggregation dis-
tribution, slow convergence speed, low precision, and proneness to fall into local optimality of the
chimpanzee search algorithm. Firstly, the improved sine chaotic mapping is used to initialize the
population to solve the population boundary aggregation distribution problem. Secondly, a linear
weighting factor and an adaptive acceleration factor are added to join the particle swarm idea and
cooperate with the improved nonlinear convergence factor to balance the global search ability of
the algorithm, accelerate the convergence of the algorithm, and improve the convergence accuracy.
Finally, the sparrow elite mutation and Bernoulli chaos mapping strategy improved by adaptive
change water wave factor are added to improve the ability of individuals to jump out of the local
optimum. Through the comparative analysis of benchmark functions seeking optimization and the
comparison of Wilcoxon rank sum statistical test seeking results, it can be seen that the IMSChoA
optimization algorithm has stronger robustness and applicability. Further, the IMSChoA optimiza-
tion algorithm is applied to two engineering examples to verify the superiority of the IMSChoA
optimization algorithm in dealing with mechanical structure optimization design problems.
Keywords: improved sine chaos mapping; nonlinear decay factor; sparrow elite mutation
1. Introduction
Meta-heuristic algorithms are widely used in path planning [
1
], image detection [
2
],
system control [
3
], and shop floor scheduling [
4
] due to their excellent flexibility, practicality,
and robustness. Common meta-heuristic algorithms include the genetic algorithm (GA) [
5
,
6
],
the particle swarm optimization algorithm (PSO) [
7
,
8
], the gray wolf optimization algorithm
(GWO) [
9
,
10
], the chicken flock optimization algorithm (CSO) [
11
], the sparrow optimization
algorithm (CSA) [12], the whale optimization algorithm (WOA) [13], etc.
Different intelligent optimization algorithms exist with different search approaches,
but most of them aim at the balance between population diversity and search ability and
avoid premature maturity while ensuring convergence accuracy and speed [
14
]. In response
to the above ideas, numerous scholars have proposed improvements to the intelligent
algorithms they studied. For example, Zhi-jun Teng et al. [
15
] introduced the idea of PSO
on the basis of the gray wolf optimization algorithm, which preserved the individual
optimum while improving the ability of the algorithm to jump out of the local optimum;
Hussien A. G. et al. [
16
] proposed two transfer functions (S-shaped and V-shaped) to map
the continuous search space to the binary space, which improved the search accuracy and
speed of the whale optimization algorithm; Wang et al. [
17
] introduced a fuzzy system in
the process of chicken flock optimization algorithm, which adaptively adjusted the number
of individuals in the algorithm, as well as random factors to balance the local exploitation
performance and global search ability of the algorithm; Tian et al. [
18
] used logistic chaotic
mapping to improve the initial population quality of the particle swarm algorithm while
applying the auxiliary speed mechanism to the global optimal particles, which effectively
Machines 2023, 11, 250. https://doi.org/10.3390/machines11020250 https://www.mdpi.com/journal/machines
