多策略融合的灰狼优化算法
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作者单位:

1.中国矿业大学信息与控制工程学院;2.中国矿业大学

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中图分类号:

TP301.6

基金项目:

国家重点研发计划项目(2018YFC0808100);江苏省重点研发计划项目(BE2016046)


Grey wolf optimization algorithm based on multi-trategy fusion
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School of Information and Control Engineering,China University of Mining and Technology

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    摘要:

    灰狼优化算法是一种新兴的群体智能优化算法,已广泛应用与诸多领域。为了解决灰狼优化算法在求解复杂优化问题时存在求解精度不高、易陷入局部最优的问题,提出了一种多策略融合的改进灰狼优化算法。该算法首先利用佳点集方法均匀初始化灰狼种群,为全局搜索多样性奠定基础;然后通过差分进化算法的变异、交叉、选择操作,维持种群的多样性,提高了算法的求解精度和寻优性能;最后为了协调算法的全局探索和局部开发能力,设计了一种非线性控制参数策略,并通过分段步长更新策略来避免算法陷入局部最优。为验证该算法的有效性,选取10个标准测试函数进行仿真实验,实验结果表明,改进灰狼优化算法在求解精度和收敛速度上明显优于其他对比算法。可见,多策略融合有效提高了灰狼优化算法的性能。

    Abstract:

    The grey wolf optimization algorithm is an emerging swarm intelligence optimization algorithm, which has been widely applied in many fields. In order to solve its shortcomings in solving complex optimization problems, such as low precision and easy to fall into local optimum, an improved grey wolf optimization algorithm based on multi-strategy fusion is proposed. The algorithm first uses the good point set method to uniformly initialize the grey wolf population to lay the foundation for the global search diversity; Then through the mutation, crossover, and selection operations of the differential evolution algorithm, the diversity of the population is maintained, and the solution accuracy and optimization of the algorithm are improved; Finally, a nonlinear control parameter strategy is designed to coordinate the global exploration and local development capabilities of the algorithm, and a segmented step size update strategy is used to avoid the algorithm from falling into local optimum.In order to verify the effectiveness of the algorithm, 10 standard test functions are selected for simulation experiments. The experimental results show that the improved grey wolf optimization algorithm is obviously better than other comparison algorithms in terms of solution accuracy and convergence speed. It is concluded that the multi-strategy fusion effectively improves the performance of the grey wolf optimization algorithm.

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吴新忠,李猛,张芝超,等. 多策略融合的灰狼优化算法[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-11-11
  • 最后修改日期:2022-04-24
  • 录用日期:2022-04-30
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