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Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization

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  • Qinghua Gu
  • Xuexian Li
  • Song Jiang

Abstract

Most real-world optimization problems tackle a large number of decision variables, known as Large-Scale Global Optimization (LSGO) problems. In general, the metaheuristic algorithms for solving such problems often suffer from the “curse of dimensionality.” In order to improve the disadvantage of Grey Wolf Optimizer when solving the LSGO problems, three genetic operators are embedded into the standard GWO and a Hybrid Genetic Grey Wolf Algorithm (HGGWA) is proposed. Firstly, the whole population using Opposition-Based Learning strategy is initialized. Secondly, the selection operation is performed by combining elite reservation strategy. Then, the whole population is divided into several subpopulations for cross-operation based on dimensionality reduction and population partition in order to increase the diversity of the population. Finally, the elite individuals in the population are mutated to prevent the algorithm from falling into local optimum. The performance of HGGWA is verified by ten benchmark functions, and the optimization results are compared with WOA, SSA, and ALO. On CEC’2008 LSGO problems, the performance of HGGWA is compared against several state-of-the-art algorithms, CCPSO2, DEwSAcc, MLCC, and EPUS-PSO. Simulation results show that the HGGWA has been greatly improved in convergence accuracy, which proves the effectiveness of HGGWA in solving LSGO problems.

Suggested Citation

  • Qinghua Gu & Xuexian Li & Song Jiang, 2019. "Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization," Complexity, Hindawi, vol. 2019, pages 1-18, February.
    • Handle: RePEc:hin:complx:2653512
    DOI: 10.1155/2019/2653512
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    References listed on IDEAS

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    1. Xingguang Peng & Yapei Wu, 2018. "Enhancing Cooperative Coevolution with Selective Multiple Populations for Large-Scale Global Optimization," Complexity, Hindawi, vol. 2018, pages 1-15, July.
    2. Hvattum, Lars Magnus & Glover, Fred, 2009. "Finding local optima of high-dimensional functions using direct search methods," European Journal of Operational Research, Elsevier, vol. 195(1), pages 31-45, May.
    3. Song Jiang & Minjie Lian & Caiwu Lu & Qinghua Gu & Shunling Ruan & Xuecai Xie, 2018. "Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope," Complexity, Hindawi, vol. 2018, pages 1-13, August.
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