基于状态转移和模糊思考的迁徙优化算法

doi:10.11896／j.issn.1002-137X.2019.01.017

摘要/Abstract

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。

关键词: 模糊对立模型, 迁徙, 优化算法, 状态模型

Abstract: 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 indivi-duals 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.

Key words: Fuzzy opposite model, Migration, Optimization algorithm, State model

中图分类号:

TP301.6

钟大鉴, 冯翔, 虞慧群. 基于状态转移和模糊思考的迁徙优化算法[J]. 计算机科学, 2019, 46(1): 112-116. https://doi.org/10.11896／j.issn.1002-137X.2019.01.017

ZHONG Da-jian, FENG Xiang, YU Hui-qun. Migration Optimization Algorithm Based on State Transition and Fuzzy Thinking[J]. Computer Science, 2019, 46(1): 112-116. https://doi.org/10.11896／j.issn.1002-137X.2019.01.017

参考文献

[1]LI X,ZHANG J,YIN M.Animal migration optimization: an optimization algorithm inspired by animal migration behavior[J].Neural Computing and Applications,2014,24(7):1867-1877. [2]FORREST S.Genetic Algorithms: Principles of Natural Selection Applied to Computation[J].Science,1993,261(5123):872-878. [3]KENNEDY J,EBERHART R.Particle swarm optimization [C]//IEEE International Conference on Neural Networks,1995.Proceedings.IEEE Xplore,1995:1942-1948. [4]BONABEAU E,DORIGO M,THERAULAZ G.Inspiration for Optimization from Social Insect Behavior[J].Nature,2000,406(6791):39-42. [5]KARABOGA D,BASTURK B.A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm[J].Journal of Global Optimization,2007,39(3):459-471. [6]KARABOGA D,BASTURK B.On the performance of artificial bee colony (ABC) algorithm[J].Appl Soft Comput,2008,8(1):687-697. [7]SIMON D.Biogeography-Based Optimization[J].IEEE Transactions on Evolutionary Computation,2009,12(6):702-713. [8]ZHANG Z,QIAN S.Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problems[J].Soft Computing,2011,15(7): 1333-1349. [9]FENG X,LAU F C M,YU H.A novel bio-inspired approach based on the behavior of mosquitoes[J].Information Sciences An International Journal,2013,233(2):87-108. [10]ZHANG H,ZHU Y,CHEN H.Root growth model: a novel approach to numerical function optimization and simulation of plant root system[J].Soft Computing,2014,18(3):521-537. [11]QIU M,MING Z,LI J,et al.Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm[J].IEEE Transactions on Computers,2015,64(12):3528-3540. [12]CHENG R,JIN Y,OLHOFER M,et al.A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization[J].IEEE Transactions on Evolutionary Computation,2016,20(5):773-791. [13]CHENG R,JIN Y.A social learning particle swarm optimization algorithm for scalable optimization[J].Information Sciences,2015,291(6):43-60. [14]TAN K C,CHIAM S C,MAMUN A A,et al.Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization[J].European Journal of OperationalResearch,2009,197(2):701-713. [15]SINHA A,MALO P,DEB K,et al.Solving Bilevel Multicriterion Optimization Problems With Lower Level Decision Uncertainty[J].IEEE Transactions on Evolutionary Computation,2016,20(2):199-217. [16]LIU C Y,YAN X H,WU H.The Wolf Colony Algorithm and Its Application[J].Chinese Journal of Electronics,2011,20(2):212-216. [17]FENG X,WANG Y,YU H,et al.A Novel Intelligence Algorithm Based on the Social Group Optimization Behaviors[J].IEEE Transactions on Systems Man & Cybernetics Systems,2017,48(1):65-76. [18]WANG B.A novel artificial bee colony algorithm based on modified search strategy and generalized opposition-based learning[J].Journal of Intelligent & Fuzzy Systems,2015,28(3):1023-1037. [19]TRUJILLO L,MUÑOZ L,GALVÓN-LÓPEZ E,et al.neat,Genetic Programming: Controlling bloat naturally[J].Information Sciences,2016,10(333):21-43. [20]CUEVAS E,CORTÉS M A D,NAVARRO D A O.A Swarm Global Optimization Algorithm Inspired in the Behavior of the Social-Spider[J].Expert Systems with Applications,2013,40(16):6374-6384.

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