《计算机应用研究》|Application Research of Computers

一种采用混合策略的改进离子运动算法

Improved ions motion algorithm by using hybrid strategy

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作者 王勇,蒙丽萍,韦量
机构 1.广西民族大学 信息科学与工程学院,南宁 530006;2.广西高校复杂系统与智能计算重点实验室,南宁 530006
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文章编号 1001-3695(2018)03-0721-06
DOI 10.3969/j.issn.1001-3695.2018.03.017
摘要 为了克服离子运动算法(IMO)存在的不足,提出一种新的改进离子运动算法(IIMO)。该IIMO算法基于同类离子相互排斥而异类离子相互吸引,以及离子在液态空间中出现随机移动的特征,刻画出一种新的离子运动数学模型。选取14个比较典型的优化问题用来测试IIMO算法的性能。测试结果表明,IIMO算法比IMO和PSO算法具有更快的收敛速度、更强的局部搜索能力和全局搜索能力,IIMO算法的鲁棒性比IMO和PSO算法强。
关键词 改进离子运动算法;优化;运动能量
基金项目 国家自然科学基金资助项目(61662005)
广西民族大学引进人才科研项目(2014MDQD018)
本文URL http://www.arocmag.com/article/01-2018-03-017.html
英文标题 Improved ions motion algorithm by using hybrid strategy
作者英文名 Wang Yong, Meng Liping, Wei Liang
机构英文名 1.CollegeofInformationScience&Engineering,GuangxiUniversityforNationalities,Nanning530006,China;2.KeyLaboratoryofGuangxiHighSchoolsComplexSystem&ComputationalIntelligence,Nanning530006,China
英文摘要 Aiming at overcoming the shortcomings of the ions motion algorithm (IMO), this paper proposed a new improved ions motion algorithm (IIMO). The IIMO depicted new mathematical models of ions motion based on the characteristics of the attraction between the same kind of ions and the repulsion between the different kinds of ions, and the random movement cha-racteristics which ions appeared in the liquid space. In order to test the performance of the IIMO, experiments were carried out on fourteen typical optimization problems, and the experimental results show that the IIMO has faster convergence rate, stronger local search ability and global searching ability than that of the IMO and the PSO. The robustness of the IIMO is stronger than that of the IMO and the PSO.
英文关键词 improved ions motion algorithm(IIMO); optimization; moving energy
参考文献 查看稿件参考文献
  [1] Holland J H. Adaptation in natural and artificial systems[M] . Ann Arbor:University of Michigan Press, 1975.
[2] Kennedy J, Eberhart R C. Particle swarm optimization[C] //Proc of IEEE International Conference on Neural Networks. Piscataway:IEEE Service Center, 1995:1942-1948.
[3] Dorigo M, Maniezzo V, Colorni A. Ant system:optimization by a co-lony of cooperating agents[J] . IEEE Trans on Systems, Man, and Cybernetics, Part B:Cybernetics, 1996, 26(1):29-41.
[4] Tereshko V. Reaction-diffusion model of a honeybee colony’s foraging behaviour[C] //Proc of the 6th International Conference on Parallel Problem Solving from Nature. Berlin:Springer, 2000:807-816.
[5] Li Xiaolei, Shao Zhijiang, Qian Jixin. An optimizing method based on autonomous animats:fish-swarm algorithm[J] . Systems Engineering-Theory & Practice, 2002, 22(11):32-38.
[6] Theraulaz G, Bonabeau E, Deneubourg J L. Self-organization of hie-rarchies in animal societies:the case of the primitively eusocial wasp polistes dominulus Christ[J] . Journal of Theoretical Biology, 1995, 174(3):313-323.
[7] Theraulaz G, Bonabeau E, Deneubourg J L. Response threshold reinforcement and division of labour in insect societies[C] //Proc of Royal Society B Biological Sciences. 1998:327-332.
[8] Eusuffm M, Lansey K E. Optimization of water distribution network design using shuffled frog leaping algorithm[J] . Journal of Water Resources Planning and Management, 2003, 129(3):210-225.
[9] Jiao L C, Wang L. Anovel genetic algorithm based on immunity[J] . IEEE Trans on System Man & Cybernetic, Part A:Systems & Humans, 2000, 30(5):552-561.
[10] Krishnanand K N, Ghose D. Glowworm swarm based optimization algorithm for multimodel functions with collective robotics applications[J] . Multiagent and Grid Systems, 2006, 2(3):209-222.
[11] Yang Xinshe. A new metaheuristic bat-inspired algorithm[J] . Computer Knowledge & Technology, 2010, 284:65-74.
[12] Tang Rui, Fong S, Yang Xinshe, et al. Wolf search algorithm with ephemeral memory[C] //Proc of the 7th International Conference on Digital Information Management. [S. l. ] :IEEE Press, 2012:165-172.
[13] Moein S, Logeswaran R. KGMO:a swarm optimization algorithm based on the kinetic energy of gas molecules[J] . Information Sciences, 2014, 275(3):127-144.
[14] Mirjalili S. Dragonfly algorithm:a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems[J] . Neural Computing & Applications, 2016, 27(4):1053-1073.
[15] Wang Gaige, Suash D, Cui Zhihua. Monarch butterfly optimization[J] . Neural Computing & Applications, 2015, 26:1-20.
[16] Javidy B, Hatamlou A, Mirjalili S. Ions motion algorithm for solving optimization problems[J] . Applied Soft Computing, 2015, 32(3):72-79.
[17] Li Xiangtao, Zhang Jie, Yin Minghao. Animal migration optimization:an optimization algorithm inspired by animal migration behavior[J] . Neural Computing & Applications, 2014, 24(7-8):1867-1877.
[18] Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer[J] . Advances in Engineering Software, 2014, 69(3):46-61.
[19] Gandomi A H, Alavi A H. Krill herd:a new bio-inspired optimization algorithm[J] . Communications in Nonlinear Science and Numerical Simulation, 2012, 17(12):4831-4845.
[20] Meng Xianbing, Liu Yu, Gao Xiaozhi, et al. A new bio-inspired algorithm:chicken swarm optimization[C] //Advances in Swarm Intelligence. New York:Springer International Publishing, 2014:86-94.
[21] Storn R, Price K. Differential evolution:a simple and efficient heuristic for global optimization over continuous spaces[J] . Journal Global Optimization, 1997, 11(4):341-359.
[22] Yao X, Liu Y, Lin G. Evolutionary programming made faster[J] . IEEE Trans on Evolutionary Computation, 1999, 3(2):82-102.
[23] Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA:a gravitational search algorithm[J] . Information Sciences, 2009, 179(13):2232-2248.
[24] Kaveh A, Khayatazad M. A new meta-heuristic method:ray optimization[J] . Computers and Structures, 2012, 112:283-294.
[25] Cuevas E, Echavarría A, Ramírez-Ortegón M A. An optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation[J] . Applied Intelligence, 2014, 40(2):256-272.
[26] Hatamlou A. Black hole:a new heuristic optimization approach for data clustering[J] . Information Sciences, 2013, 222(3):175-184.
[27] Silberberg M S. Principles of general chemistry[M] . New York:McGraw-Hill, 2007.
[28] 化学研究实验室. 化学[M] . 北京:人民教育出版社, 2008.
收稿日期 2016/11/6
修回日期 2017/1/3
页码 721-726
中图分类号 TP18;TP301.6
文献标志码 A