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A Hybrid Global Optimization Algorithm Based on Wind Driven Optimization and Differential Evolution

Author

Listed:
  • Zongfan Bao
  • Yongquan Zhou
  • Liangliang Li
  • Mingzhi Ma

Abstract

This paper presents a new hybrid global optimization algorithm, which is based on the wind driven optimization (WDO) and differential evolution (DE), named WDO-DE algorithm. The WDO-DE algorithm is based on a double population evolution strategy, the individuals in a population evolved by wind driven optimization algorithm, and a population of individuals evolved from difference operation. The populations of individuals both in WDO and DE employ an information sharing mechanism to implement coevolution. This paper chose fifteen benchmark functions to have a test. The experimental results show that the proposed algorithm can be feasible in both low-dimensional and high-dimensional cases. Compared to GA-PSO, WDO, DE, PSO, and BA algorithm, the convergence speed and precision of WDO-DE are higher. This hybridization showed a better optimization performance and robustness and significantly improves the original WDO algorithm.

Suggested Citation

  • Zongfan Bao & Yongquan Zhou & Liangliang Li & Mingzhi Ma, 2015. "A Hybrid Global Optimization Algorithm Based on Wind Driven Optimization and Differential Evolution," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-20, October.
    • Handle: RePEc:hin:jnlmpe:389630
    DOI: 10.1155/2015/389630
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    Cited by:

    1. Hamed Safayenikoo & Fatemeh Nejati & Moncef L. Nehdi, 2022. "Indirect Analysis of Concrete Slump Using Different Metaheuristic-Empowered Neural Processors," Sustainability, MDPI, vol. 14(16), pages 1-16, August.
    2. Hisham Alghamdi & Ghulam Hafeez & Sajjad Ali & Safeer Ullah & Muhammad Iftikhar Khan & Sadia Murawwat & Lyu-Guang Hua, 2023. "An Integrated Model of Deep Learning and Heuristic Algorithm for Load Forecasting in Smart Grid," Mathematics, MDPI, vol. 11(21), pages 1-22, November.

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