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Simulated Annealing-Based Krill Herd Algorithm for Global Optimization

Author

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  • Gai-Ge Wang
  • Lihong Guo
  • Amir Hossein Gandomi
  • Amir Hossein Alavi
  • Hong Duan

Abstract

Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH) method is proposed for optimization tasks. A new krill selecting (KS) operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.

Suggested Citation

  • Gai-Ge Wang & Lihong Guo & Amir Hossein Gandomi & Amir Hossein Alavi & Hong Duan, 2013. "Simulated Annealing-Based Krill Herd Algorithm for Global Optimization," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-11, June.
    • Handle: RePEc:hin:jnlaaa:213853
    DOI: 10.1155/2013/213853
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    Cited by:

    1. Yassin Belkourchia & Mohamed Zeriab Es-Sadek & Lahcen Azrar, 2023. "New Hybrid Perturbed Projected Gradient and Simulated Annealing Algorithms for Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 438-475, May.

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