文献类型：期刊文献

中文题名：基于状态转移和模糊思考的迁徙优化算法

英文题名：Migration Optimization Algorithm Based on State Transition and Fuzzy Thinking

作者：钟大鉴[1];冯翔[1];虞慧群[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2019

卷号：46

期号：1

起止页码：112

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

基金：国家自然科学基金(61472139;61462073);上海市经信委信息化发展专项资金(201602008)资助

语种：中文

中文关键词：状态模型;模糊对立模型;迁徙;优化算法

外文关键词：State model;Fuzzy opposite model;Migration;Optimization algorithm

摘要：基于现有的动物迁徙算法(AMO),提出基于状态转移和模糊思考的迁徙优化算法(SMO)来解决全局优化问题。SMO算法中引入了状态模型和模糊对立模型。首先,状态模型中使用两种状态(分散状态和集中状态)来描述种群分布。在分散状态下,群体随机分布于解空间中,因此,使用概率决策的方式探索解空间,这个过程属于空间探索;随着个体之间的相互学习,个体之间的差异已经很小,群体进入集中状态,此时使用基于步长的搜索策略来调节个体位置,这个过程属于局部勘探。因此,将二者结合可以平衡空间探索和局部勘探功能。其次,算法使用了模糊对立模型,充分利用个体的模糊对立位置,增加了群体的多样性,提高了算法的收敛精度。然后,从理论上证明了该算法的收敛性,并且使用12个基准测试函数来验证算法的性能。最后,将该算法与其他优化算法进行比较,实验结果验证了该算法在优化问题上的有效性。
Inspired by the existing animal migration optimization algorithm(AMO),a novel migration optimization algorithm based on state transition and fuzzy thinking(SMO)was proposed for solving global optimization problems.In the proposed algorithm,the state model and fuzzy opposite model are constructed.Firstly,the state model describes the distribution of the whole group with two states:the dispersed state and the centralized state.In the dispersed state,the whole group is distributed in the solution space randomly and a probabilistic decision-making method is used to search the solution space.It’s the process of exploration.As the individuals learning from each other,the differences between individuals become smaller and smaller,and the state of the group changes into the centralized state.Meanwhile,a step based searching strategy is used to find the optimal value.It’s the process of exploitation.Therefore,the balance between exploration and exploitation can be obtained by using different searching strategies according to the state of the group.Secondly,the algorithm uses a fuzzy opposite model.It can make full use of the fuzzy opposite position of individuals and increase the diversity of species.Moreover,it can improve the convergence precision of the algorithm.Then,the convergence of the algorithm is proved theoretically,and twelve benchmark functions are used to verify the perfor-mance of the proposed algorithm.Finally,the algorithm is compared with three other optimization algorithms.Experimental results attest to the effectiveness of SMO.