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Impact of Chaos Functions on Modern Swarm Optimizers

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  • E Emary
  • Hossam M Zawbaa

Abstract

Exploration and exploitation are two essential components for any optimization algorithm. Much exploration leads to oscillation and premature convergence while too much exploitation slows down the optimization algorithm and the optimizer may be stuck in local minima. Therefore, balancing the rates of exploration and exploitation at the optimization lifetime is a challenge. This study evaluates the impact of using chaos-based control of exploration/exploitation rates against using the systematic native control. Three modern algorithms were used in the study namely grey wolf optimizer (GWO), antlion optimizer (ALO) and moth-flame optimizer (MFO) in the domain of machine learning for feature selection. Results on a set of standard machine learning data using a set of assessment indicators prove advance in optimization algorithm performance when using variational repeated periods of declined exploration rates over using systematically decreased exploration rates.

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  • E Emary & Hossam M Zawbaa, 2016. "Impact of Chaos Functions on Modern Swarm Optimizers," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-26, July.
  • Handle: RePEc:plo:pone00:0158738
    DOI: 10.1371/journal.pone.0158738
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    Cited by:

    1. Elena Niculina Dragoi & Vlad Dafinescu, 2021. "Review of Metaheuristics Inspired from the Animal Kingdom," Mathematics, MDPI, vol. 9(18), pages 1-52, September.
    2. Shinohara, Shuji & Okamoto, Hiroshi & Manome, Nobuhito & Gunji, Pegio-Yukio & Nakajima, Yoshihiro & Moriyama, Toru & Chung, Ung-il, 2022. "Simulation of foraging behavior using a decision-making agent with Bayesian and inverse Bayesian inference: Temporal correlations and power laws in displacement patterns," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    3. Yi Cui & Ronghua Shi & Jian Dong, 2022. "CLTSA: A Novel Tunicate Swarm Algorithm Based on Chaotic-Lévy Flight Strategy for Solving Optimization Problems," Mathematics, MDPI, vol. 10(18), pages 1-39, September.
    4. Esteban Tlelo-Cuautle & Antonio de Jesus Quintas-Valles & Luis Gerardo de la Fraga & Jose de Jesus Rangel-Magdaleno, 2016. "VHDL Descriptions for the FPGA Implementation of PWL-Function-Based Multi-Scroll Chaotic Oscillators," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-32, December.
    5. Abdelhady Ramadan & Salah Kamel & Mohamed H. Hassan & Marcos Tostado-Véliz & Ali M. Eltamaly, 2021. "Parameter Estimation of Static/Dynamic Photovoltaic Models Using a Developed Version of Eagle Strategy Gradient-Based Optimizer," Sustainability, MDPI, vol. 13(23), pages 1-29, November.

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