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Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information

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  • Fu Yan
  • Jianzhong Xu
  • Kumchol Yun

Abstract

The grey wolf optimizer (GWO) algorithm is a recently developed, novel, population-based optimization technique that is inspired by the hunting mechanism of grey wolves. The GWO algorithm has some distinct advantages, such as few algorithm parameters, strong global optimization ability, and ease of implementation on a computer. However, the paramount challenge is that there are some cases where the GWO is prone to stagnation in local optima. This drawback of the GWO algorithm may be attributed to an insufficiency in its position-updated equation, which disregards the positional interaction information about the three best grey wolves (i.e., the three leaders). This paper proposes an improved version of the GWO algorithm that is based on a dynamically dimensioned search, spiral walking predation technique, and positional interaction information (referred to as the DGWO). In addition, a nonlinear control parameter strategy, i.e., the control parameter that is nonlinearly increased with an increase in iterations, is designed to balance the exploration and exploitation of the GWO algorithm. The experimental results for 23 general benchmark functions and 3 well-known engineering optimization design applications validate the effectiveness and feasibility of the proposed DGWO algorithm. The comparison results for the 23 benchmark functions show that the proposed DGWO algorithm performs significantly better than the GWO and its improved variant for most benchmarks. The DGWO provides the highest solution precision, strongest robustness, and fastest convergence rate among the compared algorithms in almost all cases.

Suggested Citation

  • Fu Yan & Jianzhong Xu & Kumchol Yun, 2019. "Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information," Complexity, Hindawi, vol. 2019, pages 1-36, December.
  • Handle: RePEc:hin:complx:7189653
    DOI: 10.1155/2019/7189653
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    References listed on IDEAS

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    1. An-qing Xing, 1989. "Applications of the penalty function method in constrained optimal control problems," International Journal of Stochastic Analysis, Hindawi, vol. 2, pages 1-15, January.
    2. Jianzhong Xu & Fu Yan & Kumchol Yun & Lifei Su & Fengshu Li & Jun Guan, 2019. "Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 12(12), pages 1-26, June.
    3. Gabriel M. Portal & Marcus Ritt & Leonardo M. Borba & Luciana S. Buriol, 2016. "Simulated annealing for the machine reassignment problem," Annals of Operations Research, Springer, vol. 242(1), pages 93-114, July.
    4. Shinuk Kang & Sangho Lee & Taeuk Kang, 2017. "Development and Application of Storage-Zone Decision Method for Long-Term Reservoir Operation Using the Dynamically Dimensioned Search Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 219-232, January.
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