CPPE: An Improved Phasmatodea Population Evolution Algorithm with Chaotic Maps

The Phasmatodea Population Evolution (PPE) algorithm, inspired by the evolution of the phasmatodea population, is a recently proposed meta-heuristic algorithm that has been applied to solve problems in engineering. Chaos theory has been increasingly applied to enhance the performance and convergence...

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Bibliographic Details
Published inMathematics (Basel) Vol. 11; no. 9; p. 1977
Main Authors Wu, Tsu-Yang, Li, Haonan, Chu, Shu-Chuan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 22.04.2023
Subjects
Algorithms
Chaos theory
chaotic maps
chaotic-based PPE algorithm
Convergence
Evolutionary algorithms
Fertility
Genetic algorithms
Growth models
Heuristic
Heuristic methods
Impact factors
Mathematics
meta-heuristic algorithm
Optimization algorithms
Performance evaluation
Predictions
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Summary:The Phasmatodea Population Evolution (PPE) algorithm, inspired by the evolution of the phasmatodea population, is a recently proposed meta-heuristic algorithm that has been applied to solve problems in engineering. Chaos theory has been increasingly applied to enhance the performance and convergence of meta-heuristic algorithms. In this paper, we introduce chaotic mapping into the PPE algorithm to propose a new algorithm, the Chaotic-based Phasmatodea Population Evolution (CPPE) algorithm. The chaotic map replaces the initialization population of the original PPE algorithm to enhance performance and convergence. We evaluate the effectiveness of the CPPE algorithm by testing it on 28 benchmark functions, using 12 different chaotic maps. The results demonstrate that CPPE outperforms PPE in terms of both performance and convergence speed. In the performance analysis, we found that the CPPE algorithm with the Tent map showed improvements of 8.9647%, 10.4633%, and 14.6716%, respectively, in the Final, Mean, and Standard metrics, compared to the original PPE algorithm. In terms of convergence, the CPPE algorithm with the Singer map showed an improvement of 65.1776% in the average change rate of fitness value, compared to the original PPE algorithm. Finally, we applied our CPPE to stock prediction. The results showed that the predicted curve was relatively consistent with the real curve.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11091977