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CPPE: An Improved Phasmatodea Population Evolution Algorithm with Chaotic Maps

Author

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  • Tsu-Yang Wu

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Haonan Li

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Shu-Chuan Chu

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

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.

Suggested Citation

  • Tsu-Yang Wu & Haonan Li & Shu-Chuan Chu, 2023. "CPPE: An Improved Phasmatodea Population Evolution Algorithm with Chaotic Maps," Mathematics, MDPI, vol. 11(9), pages 1-21, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:1977-:d:1129973
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    References listed on IDEAS

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    1. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    2. Chen, Xiao & Cao, Benyi & Pouramini, Somayeh, 2023. "Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study," Energy, Elsevier, vol. 270(C).
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    Cited by:

    1. Jian Dong, 2023. "Preface to the Special Issue on “Recent Advances in Swarm Intelligence Algorithms and Their Applications”—Special Issue Book," Mathematics, MDPI, vol. 11(12), pages 1-4, June.
    2. Chin Soon Ku & Jiale Xiong & Yen-Lin Chen & Shing Dhee Cheah & Hoong Cheng Soong & Lip Yee Por, 2023. "Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market," Mathematics, MDPI, vol. 11(11), pages 1-20, May.

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