Rank-based differential evolution with multiple mutation strategies for large scale global optimization
Rank-based differential evolution with multiple mutation strategies for large scale global optimization
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DOI：
10.1109/cec.2015.7256913
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
J. Kushida;Akira Hara;T. Takahama
中科院分区：
文献类型：
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作者：
J. Kushida;Akira Hara;T. Takahama
差分进化算法是进化算法领域中最强大的全局数值优化算法之一。然而，DE的性能受到控制参数和变异策略的影响。此外，控制参数和变异策略的选择强烈依赖于优化问题的特性。因此，研究集中在控制的参数和突变策略是目前一个活跃的研究领域。其中之一，DE与景观模态检测（LMDEa），检测景观模态使用当前搜索点，表现出优异的性能，为大规模优化问题。在我们的研究中，我们通过引入基于秩的差分进化（RDE）的概念来改进LMDEa。该方法利用搜索点的排序信息，以分配一个合适的比例因子（F）和交叉率（CR）为每个人。此外，多个变异策略，此外，他们也被分配的排名信息，以实现一个良好的平衡的探索和开发能力。通过实验，使用基准函数集，我们证明了所提出的方法的有效性。
Differential Evolution (DE) is one of the most powerful global numerical optimization algorithms in the field of evolutionary algorithm. However, the performance of DE is affected by control parameters and mutation strategies. In addition, the choice of the control parameters and mutation strategies is strongly dependent on the characteristics of optimization problems. As a result, studies focused on controlling the parameters and mutation strategies is currently an active area of research. One of them, DE with landscape modality detection (LMDEa) which detects the landscape modality using the current search points, showed excellent performance for large scale optimization problem. In our research, we improve LMDEa by introducing the concept of Rank-based Differential Evolution (RDE). The proposed method utilizes ranking information of search points in order to assign a suitable scaling factor (F) and a crossover rate (CR) for each individual. Furthermore multiple mutation strategies are employed; in addition, they are also assigned by the ranking information for realizing a well-balanced exploration and exploitation ability. Through experimentation, using the set of benchmark functions, we show the effectiveness of the proposed method.