Due to its simplicity and ease of use, the standard grey wolf optimizer （GWO） is attracting much attention. However, due to its imperfect search structure and possible risk of being trapped in local optima, its appl...Due to its simplicity and ease of use, the standard grey wolf optimizer （GWO） is attracting much attention. However, due to its imperfect search structure and possible risk of being trapped in local optima, its application has been limited. To perfect the performance of the algorithm, an optimized GWO is proposed based on a mutation operator and eliminating-reconstructing mechanism （MR-GWO）. By analyzing GWO, it is found that it conducts search with only three leading wolves at the core, and balances the exploration and exploitation abilities by adjusting only the parameter a, which means the wolves lose some diversity to some extent. Therefore, a mutation operator is introduced to facilitate better searching wolves, and an eliminating- reconstructing mechanism is used for the poor search wolves, which not only effectively expands the stochastic search, but also accelerates its convergence, and these two operations complement each other well. To verify its validity, MR-GWO is applied to the global optimization experiment of 13 standard continuous functions and a radial basis function （RBF） network approximation experiment. Through a comparison with other algorithms, it is proven that MR-GWO has a strong advantage.展开更多
基金supported by the National High-Tech R&D Program(863)of China(No.2015AA7041003)the Scientific Research Plan Projects of Shanxi Education Department(No.17JK0825)the Scientific Research Plan Projects of Xianyang Normal University(No.15XSYK036)
文摘Due to its simplicity and ease of use, the standard grey wolf optimizer （GWO） is attracting much attention. However, due to its imperfect search structure and possible risk of being trapped in local optima, its application has been limited. To perfect the performance of the algorithm, an optimized GWO is proposed based on a mutation operator and eliminating-reconstructing mechanism （MR-GWO）. By analyzing GWO, it is found that it conducts search with only three leading wolves at the core, and balances the exploration and exploitation abilities by adjusting only the parameter a, which means the wolves lose some diversity to some extent. Therefore, a mutation operator is introduced to facilitate better searching wolves, and an eliminating- reconstructing mechanism is used for the poor search wolves, which not only effectively expands the stochastic search, but also accelerates its convergence, and these two operations complement each other well. To verify its validity, MR-GWO is applied to the global optimization experiment of 13 standard continuous functions and a radial basis function （RBF） network approximation experiment. Through a comparison with other algorithms, it is proven that MR-GWO has a strong advantage.
