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A chaos-based evolutionary algorithm for general nonlinear programming problems

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  • El-Shorbagy, M.A.
  • Mousa, A.A.
  • Nasr, S.M.

Abstract

In this paper we present a chaos-based evolutionary algorithm (EA) for solving nonlinear programming problems named chaotic genetic algorithm (CGA). CGA integrates genetic algorithm (GA) and chaotic local search (CLS) strategy to accelerate the optimum seeking operation and to speed the convergence to the global solution. The integration of global search represented in genetic algorithm and CLS procedures should offer the advantages of both optimization methods while offsetting their disadvantages. By this way, it is intended to enhance the global convergence and to prevent to stick on a local solution. The inherent characteristics of chaos can enhance optimization algorithms by enabling it to escape from local solutions and increase the convergence to reach to the global solution. Twelve chaotic maps have been analyzed in the proposed approach. The simulation results using the set of CEC’2005 show that the application of chaotic mapping may be an effective strategy to improve the performances of EAs.

Suggested Citation

  • El-Shorbagy, M.A. & Mousa, A.A. & Nasr, S.M., 2016. "A chaos-based evolutionary algorithm for general nonlinear programming problems," Chaos, Solitons & Fractals, Elsevier, vol. 85(C), pages 8-21.
    • Handle: RePEc:eee:chsofr:v:85:y:2016:i:c:p:8-21
    DOI: 10.1016/j.chaos.2016.01.007
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Mohammed A. El-Shorbagy & Fatma M. Al-Drees, 2023. "Studying the Effect of Introducing Chaotic Search on Improving the Performance of the Sine Cosine Algorithm to Solve Optimization Problems and Nonlinear System of Equations," Mathematics, MDPI, vol. 11(5), pages 1-25, March.
    2. Abdelsalam, Ali M. & El-Shorbagy, M.A., 2018. "Optimization of wind turbines siting in a wind farm using genetic algorithm based local search," Renewable Energy, Elsevier, vol. 123(C), pages 748-755.
    3. Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2022. "Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem," Energies, MDPI, vol. 15(13), pages 1-26, June.
    4. M. A. El-Shorbagy & A. Y. Ayoub & A. A. Mousa & I. M. El-Desoky, 2019. "An enhanced genetic algorithm with new mutation for cluster analysis," Computational Statistics, Springer, vol. 34(3), pages 1355-1392, September.
    5. M. A. El-Shorbagy & A. A. Mousa & M. A. Farag, 2019. "An intelligent computing technique based on a dynamic-size subpopulations for unit commitment problem," OPSEARCH, Springer;Operational Research Society of India, vol. 56(3), pages 911-944, September.
    6. Mohammed A. El-Shorbagy & Islam M. Eldesoky & Mohamady M. Basyouni & Islam Nassar & Adel M. El-Refaey, 2022. "Chaotic Search-Based Salp Swarm Algorithm for Dealing with System of Nonlinear Equations and Power System Applications," Mathematics, MDPI, vol. 10(9), pages 1-30, April.
    7. Muhammad Nabeel Hussain & Nadeem Shaukat & Ammar Ahmad & Muhammad Abid & Abrar Hashmi & Zohreh Rajabi & Muhammad Atiq Ur Rehman Tariq, 2022. "Micro-Siting of Wind Turbines in an Optimal Wind Farm Area Using Teaching–Learning-Based Optimization Technique," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    8. Khan, Taimoor Ali & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Mehmood, Khizer & Hsu, Chung-Chian & Raja, Muhammad Asif Zahoor, 2024. "Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    9. Muhammad Nabeel Hussain & Nadeem Shaukat & Ammar Ahmad & Muhammad Abid & Abrar Hashmi & Zohreh Rajabi & Muhammad Atiq Ur Rehman Tariq, 2022. "Effective Realization of Multi-Objective Elitist Teaching–Learning Based Optimization Technique for the Micro-Siting of Wind Turbines," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    10. Shen, Yanqing, 2018. "Improved chaos genetic algorithm based state of charge determination for lithium batteries in electric vehicles," Energy, Elsevier, vol. 152(C), pages 576-585.

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