基于柯西分布和父种轮换机制的种子优化算法

doi:10.16451/j.cnki.issn1003-6059.202107001

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摘要

针对目前种子优化算法存在的空间探索能力不足、后代个体分布多样性较弱的问题,文中提出基于柯西分布和父种轮换机制的种子优化算法.首先,构建基于柯西分布的种群分布模型,用于前期探索阶段,提升算法的全局搜索能力.然后,建立基于赌轮法的父种轮换机制,提高后代种群的多样性.最后,构建个体距离阈值、分布方差和后代比例的自适应调整机制,提高算法对复杂优化问题的动态寻优能力.实验表明,文中算法的平均适应度值和弗里德曼检测指标排名均较优.

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	刘航
	张晓明
	汪长剑

关键词 ：群体智能, 种子优化算法, 柯西分布, 父种轮换, 粒子群优化

Abstract：

To improve the weak spatial exploration ability and the poor diversity of individual distribution of offspring in current bean optimization algorithm, a bean optimization algorithm based on Cauchy distribution and parent rotation mechanism(BOA-CPR) is presented. Firstly, a population distribution model based on Cauchy distribution is constructed and used in the exploration phase to improve the global search ability of the algorithm. Then, a parent rotation mechanism based on the roulette wheel selection is established to enhance the diversity of offspring. Finally, an adaptive adjustment mechanism for three important parameters, including individual distance threshold, distribution variance and descendant ratio, is designed to improve the dynamic optimization ability of the algorithm for complex optimization problems. Experiments show that BOA-CPR ranks better in average fitness value and Friedman detection index.

Key words： Swarm Intelligence Bean Optimization Algorithm Cauchy Distribution Parent Rotation Particle Swarm Optimization

收稿日期: 2021-05-13

ZTFLH:

TP18

基金资助:

国家重点研发计划项目(No.2017YFD0301303)、安徽省重点研发计划项目(No.201904e01020015)、青海省基础研究计划项目(No.2020-0302-ZJC-913)、青海省重点研发与转化计划项目(No.2020-0206-0007)资助

通讯作者: 张晓明,博士,副教授,主要研究方向为群体智能、智能决策.E-mail:xmzhang@ustc.edu.

作者简介: 刘航,硕士研究生,主要研究方向为群体智能.E-mail:q20301205@stu.ahu.edu.cn.汪长剑,硕士研究生,主要研究方向为群体智能算法及其应用.E-mail:q20301204@stu.ahu.edu.cn.

引用本文:

刘航, 张晓明, 汪长剑. 基于柯西分布和父种轮换机制的种子优化算法[J]. 模式识别与人工智能, 2021, 34(7): 581-591. LIU Hang, ZHANG Xiaoming, WANG Changjian. Bean Optimization Algorithm Based on Cauchy Distribution and Parent Rotation Mechanism. , 2021, 34(7): 581-591.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.202107001 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2021/V34/I7/581

[1] BENI G, WANG J. Swarm Intelligence in Cellular Robotic Systems//DARIO P, SANDINI G, AEBISCHER P, eds. Robots and Biological Systems: Towards a New Bionics? Berlin, Germany: Sprin-ger, 1989: 703-712.
[2] 朱经纬,方虎生,邵发明,等.具有自适应弹射机制的粒子群算法.模式识别与人工智能, 2019, 32(2): 108-116.
(ZHU J W, FANG H S, SHAO F M, et al. Self-adaptive Ejector Particle Swarm Optimization Algorithm. Pattern Recognition and Artificial Intelligence, 2019, 32(2): 108-116.)
[3] ELBELTAGI E, HEGAZY T, GRIERSON D. Comparison among Five Evolutionary-Based Optimization Algorithms. Advanced Engineering Informatics, 2005, 19(1): 43-53.
[4] 杨启文,蔡亮,薛云灿.差分进化算法综述.模式识别与人工智能, 2008, 21(4): 506-513.
(YANG Q W, CAI L, XUE Y C. A Survey of Differential Evolution Algorithms. Pattern Recognition and Artificial Intelligence, 2008, 21(4): 506-513.)
[5] LI Z H, ZHANG Q F, LIN X, et al. Fast Covariance Matrix Adaptation for Large-Scale Black-Box Optimization. IEEE Transactions on Cybernetics, 2020, 50(5): 2073-2083.
[6] 张晓明,王儒敬,宋良图.一种新的进化算法——种子优化算法.模式识别与人工智能, 2008, 21(5): 677-681.
(ZHANG X M, WANG R J, SONG L T. A Novel Evolutionary Algorithm-Seed Optimization Algorithm. Pattern Recognition and Artificial Intelligence, 2008, 21(5): 677-681.)
[7] ZHANG X M, SUN B Y, MEI T, et al. Post-Disaster Restoration Based on Fuzzy Preference Relation and Bean Optimization Algorithm//Proc of the IEEE Youth Conference on Information, Computing and Telecommunications. Washington, USA: IEEE, 2010: 271-274.
[8] ZHANG X M, WANG H L, SUN B Y, et al. The Markov Model of Bean Optimization Algorithm and Its Convergence Analysis. International Journal of Computational Intelligence Systems, 2013, 6(4): 609-615.
[9] FENG T H, XIE Q L, HU H Y, et al. Bean Optimization Algorithm Based on Negative Binomial Distribution//Proc of the International Conference in Swarm Intelligence. Berlin, Germany: Springer, 2015: 82-88.
[10] ZHANG X M, FENG T H. Chaotic Bean Optimization Algorithm. Soft Computing, 2018, 22(1): 67-77.
[11] ZHANG X M, HU Y Q, LI T J. A Novel Target Searching Algorithm for Swarm UAVs Inspired from Spatial Distribution Patterns of Plant Population. International Journal of Computational Intelligence Systems, 2020, 14(1): 159-167.
[12] BURI A, CIANFRANI C, PINTO-FIGUEROA E, et al. Soil Factors Improve Predictions of Plant Species Distribution in a Mountain Environment. Progress in Physical Geography, 2017, 41(6): 703-722.
[13] IVERSON L R, MCKENZIE D. Tree-Species Range Shifts in a Changing Climate: Detecting, Modeling, Assisting. Landscape Ecology, 2013, 28(5): 879-889.
[14] WIEGAND T, MOLONEY K A. Rings, Circles, and Null-Models for Point Pattern Analysis in Ecology. Oikos, 2004, 104(2): 209-229.
[15] ZHANG X M, SUN B Y, MEI T, et al. A Novel Evolutionary Algorithm Inspired by Beans Dispersal. International Journal of Computational Intelligence Systems, 2013, 6(1): 79-86.
[16] 逯少华,张晓伟,鲍承强,等.柯西种群分布的自适应范围粒子群优化算法.计算机应用, 2014, 34(4): 1070-1073.
(LU S H, ZHANG X W, BAO C Q, et al. Adaptive Range Particle Swarm Optimization with the Cauchy Distributed Population. Computer Applications, 2014, 34(4): 1070-1073, 1079.)
[17] DAI W, AU O C, LI S J, et al. Adaptive Search Range Algorithm Based on Cauchy Distribution//Proc of the Visual Communications and Image Processing. Washington, USA: IEEE, 2012. DOI: 10.1109/VCIP.2012.6410741.
[18] JONES M C, FADDY M J. A Skew Extension of the t-Distribution, with Applications. Journal of the Royal Statistical Society Series B, 2003, 65(1): 159-174.
[19] HODGE B M, MILLIGAN M. Wind Power Forecasting Error Distributions over Multiple Timescales//Proc of the IEEE Power and Energy Society General Meeting. Washington, USA: IEEE, 2011. DOI: 10.1109/PES.2011.6039388.
[20] 胡新平,贺玉芝,倪巍伟,等.基于赌轮选择遗传算法的数据隐藏发布方法.计算机研究与发展, 2012, 49(11): 2432-2439.
(HU X P, HE Y Z, NI W W, et al. A Privacy-Preserving Data Publishing Method Based on Genetic Algorithm with Roulette Wheel. Computer Research and Development, 2012, 49(11): 2432-2439.)
[21] LIANG J, QU B Y, SUGANTHAN P N, et al. Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization. Technical Report, 201212. Zhengzhou, China: Zhengzhou University, 2013.
[22] LAZZUS J A. Modeling of Gas-Solid Equilibrium of Binary Systems Aided by Frankenstein PSO. Comptes Rendus Chimie, 2016, 19(5): 630-638.
[23] DARWISH A, EZZAT D, HASSANIEN A E. An Optimized Model Based on Convolutional Neural Networks and Orthogonal Learning Particle Swarm Optimization Algorithm for Plant Diseases Diagnosis. Swarm and Evolutionary Computation, 2020, 52. DOI:10.1016/j.swevo.2019.100616.
[24] WANG S H, LI Y Z, YANG H Y. Self-adaptive Mutation Diffe-rential Evolution Algorithm Based on Particle Swarm Optimization. Applied Soft Computing, 2019, 81. DOI:10.1016/j.asoc.2019.105496.
[25] JIN X, LIANG Y Q, TIAN D P, et al. Particle Swarm Optimization Using Dimension Selection Methods. Applied Mathematics and Computation, 2013, 219(10): 5185-5197.
[26] LI Y H, ZHAN Z H, LIN S J, et al. Competitive and Cooperative Particle Swarm Optimization with Information Sharing Mechanism for Global Optimization Problems. Information Sciences, 2015, 293(3): 370-382.
[27] TANWEER M R, SURESH S, SUNDARARAJAN N. Improved SRPSO Algorithm for Solving CEC 2015 Computationally Expensive Numerical Optimization Problems//Proc of the IEEE Congress on Evolutionary Computation. Washington, USA: IEEE, 2015: 1943-1949.
[28] CAO Y L, ZHANG H, LI W F, et al. Comprehensive Learning Particle Swarm Optimization Algorithm with Local Search for Multimodal Functions. IEEE Transactions on Evolutionary Computation, 2019, 23(4): 718-731.
[29] LIN A P, SUN W, YU H S, et al. Global Genetic Learning Particle Swarm Optimization with Diversity Enhancement by Ring Topo-logy. Swarm and Evolutionary Computation, 2019, 44: 571-583.
[30] USMAN M, AMIN A, MUHAMMAD M A, et al. Optimal under Voltage Load Shedding Scheme for a Distribution Network Using EPSO Algorithm//Proc of the 1st International Conference on Power, Energy and Smart Grid. Washington, USA: IEEE, 2018. DOI: 10.1109/ICPESG.2018.8384525.
[31] XIA X W, GUI L, HE G L, et al. An Expanded Particle Swarm Optimization Based on Multi-exemplar and Forgetting Ability. Information Sciences, 2020, 508: 105-120.
[32] 宋威,华子彧.融入社会影响力的粒子群优化算法.计算机科学与探索, 2020, 14(11): 1908-1919.
(SONG W, HUA Z Y. Particle Swarm Optimization with Social Influence. Journal of Frontiers of Computer Science and Technology, 2020, 14(11): 1908-1919.)
[33] TRIVEDI A, SANYAL K, VERMA P, et al. A Unified Different-ial Evolution Algorithm for Constrained Optimization Problems//Proc of the IEEE Congress on Evolutionary Computation. Washington, USA: IEEE, 2017: 1231-1238.
[34] ELSAYED S M, SARKER R A, ESSAM D L. A Genetic Algorithm for Solving the CEC′2013 Competition Problems on Real-Parameter Optimization//Proc of the IEEE Congress on Evolutio-nary Computation. Washington, USA: IEEE, 2013: 356-360.
[35] LI G H, CUI L Z, FU X H, et al. Artificial Bee Colony Algorithm with Gene Recombination for Numerical Function Optimization. Applied Soft Computing, 2017, 52: 146-159.
[36] SUTTORP T, HANSEN N, IGEL C. Efficient Covariance Matrix Update for Variable Metric Evolution Strategies. Machine Lear-ning, 2009, 75: 167-197.