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Chaotic Fruit Fly Algorithm for Solving Engineering Design Problems

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  • M. A. El-Shorbagy

Abstract

The aim of this article is to present a chaotic fruit fly algorithm (CFFA) as an optimization approach for solving engineering design problems (EDPs). In CFFA, the fruit fly algorithm (FFA), which is recognized for its durability and efficiency in addressing optimization problems, was paired with the chaotic local search (CLS) method, which allows for local exploitation. CFFA will be set up to work in two phases: in the first, FFA will be used to discover an approximate solution, and in the second, chaotic local search (CLS) will be used to locate the optimal solution. As a result, CFFA can address difficulties associated with the basic FFA such as falling into local optima, an imbalance between exploitation and exploration, and a lack of optimum solution acquisition (i.e., overcoming the drawback of premature convergence and increasing the local exploitation capability). The chaotic logistic map is employed in the CLS because it has been demonstrated to be effective in improving the quality of solutions and giving the best performance by many studies. The proposed algorithm is tested by the set of CEC’2005 special sessions on real parameter optimization and many EDPs from the most recent test suite CEC’2020. The results have demonstrated the superiority of the proposed approach to finding the global optimal solution. Finally, CFFA′s results were compared to those of earlier research, and statistical analysis using Friedman and Wilcoxon's tests revealed its superiority and capacity to tackle this type of problem.

Suggested Citation

  • M. A. El-Shorbagy, 2022. "Chaotic Fruit Fly Algorithm for Solving Engineering Design Problems," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:6627409
    DOI: 10.1155/2022/6627409
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    References listed on IDEAS

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    1. M. A. Elsisy & D. A. Hammad & M. A. El-Shorbagy, 2020. "Solving Interval Quadratic Programming Problems by Using the Numerical Method and Swarm Algorithms," Complexity, Hindawi, vol. 2020, pages 1-11, September.
    2. 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.
    3. Gaige Wang & Lihong Guo, 2013. "A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    4. Gaige Wang & Lihong Guo, 2013. "A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-21, February.
    5. Fei Ye & Xin Yuan Lou & Lin Fu Sun, 2017. "An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-36, April.
    6. Yuan, Xiaofang & Liu, Yuanming & Xiang, Yongzhong & Yan, Xinggang, 2015. "Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 1267-1281.
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