基于Tent映射的自适应混沌混合多目标遗传算法

聂瑞; 章卫国; 李广文; 刘小雄

基于Tent映射的自适应混沌混合多目标遗传算法

西北工业大学自动化学院, 西安 710072

基金项目: 航空科学基金资助项目(20090753008)

详细信息

中图分类号: TP 3
计量
- 文章访问数: 2370
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- 被引次数: 0
出版历程
- 收稿日期: 2011-04-02
- 网络出版日期: 2012-08-30

Adaptive chaos hybrid multi-objective genetic algorithm based on the Tent map

School of Automation, Northwestern Polytechnical University, Xi’an 710072, China

摘要

摘要: 提出一种Tent映射在计算机上实现的改进算法,有效解决了受计算机字长影响,Tent映射存在不动点和小周期的情况.将改进的Tent映射应用于混沌优化算法中,对基本NSGA-Ⅱ算法进行改进.使用混沌序列对初始种群赋值,提高算法收敛能力;使用改进的混沌搜索增强种群多样性;分别使用基本算法和改进算法对标准测试函数进行数值仿真.统计结果显示:改进的算法可以在保持高效率求解的同时,得到的非劣解在收敛性和多样性指标上均优于基本NSGA-Ⅱ算法.
- 多目标优化 /
- 遗传算法 /
- 混沌 /
- Tent映射 /
- 世代距离指标
Abstract: A modified algorithm which realized the tent map on the computer was proposed to deal effectively with the problem of fixed point and the small periodic of the tent map, which affected by the finite word-length of computer. The chaos research algorithm based on the modified Tent map was introduced to the multi-objective genetic algorithm. Firstly, the chaos sequence was applied to assign the initial population value to enhance the convergence ability. Then, the chaos search optimization algorithm was adopted to improve the diversity of the population. The benchmark problems were tested by the basic and the modified algorithm respectively. The statistical results show that the improved algorithm could seek the solution more efficiently, and achieve better convergence and diversity than basic NSGA-Ⅱ.
- multi-objective optimization /
- genetic algorithm /
- chaos /
- Tent map /
- generation gap distance index

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参考文献(19)

[1]	公茂果,焦李成,杨咚咚,等.进化多目标优化算法研究[J].软件学报,2009(2):271-289 Gong Maoguo,Jiao Licheng,Yang Dongdong,et al.Evolutionary multi-objective optimization algorithms[J].Journal of Software,2009,20(2):271-289(in Chinese)
[2]	Li H,Zhang Q.Multiobjective optimization problems with complicated Pareto sets,MOEA/D and NSGA-Ⅱ[J].Evolutionary Computation,2009,13(2):284-302
[3]	Deb K,Pratap A,Agarwal S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-Ⅱ[J].Evolutionary Computation,2002,6(2):182-197
[4]	Köppen M,Yoshida K.Substitute distance assignments in NSGA-Ⅱ for handling many-objective optimization problems //Shigeru Obayashi,Kalyanmoy Deb.Evolutionary Multi-Criterion Optimization.Berlin:Springer,2007:727-741
[5]	Deb K,Rao N U,Karthik S.Dynamic multi-objective optimization and decision-making using modified NSGA-Ⅱ:a case study on hydro-thermal power scheduling // Shigeru Obayashi,Kalyanmoy Deb.Evolutionary Multi-Criterion Optimization.Berlin:Springer,2007:803-817
[6]	Coello C A C,Lamont G B,Van Veldhuizen D A.Evolutionary algorithms for solving multi-objective problems[M].New York:Kluwer Academic Publishers,2002:35-39
[7]	郝柏林.从抛物线谈起[M].上海:上海科技教育出版社,1993:26-45 Hao Bolin.Speaking of parabola[M].Shanghai:Shanghai Scientific & Technological Education Publishing House,1993:26-45(in Chinese)
[8]	汪仲清,薛皓,李婧,等.基于双帐篷映射的伪混沌编码器及其解码方法[J].重庆邮电大学学报:自然科学版,2010,22(2):200-204 Wang Zhongqing,Xue Hao,Li Jing,et al.Realization scheme of pseudo-chaotic encoder and decoder based on double-tentmapping[J].Journal of Chongqing University of Posts and Telecommunications:Natural Science Edition,2010,22(2):200-204 (in Chinese)
[9]	Alatas B,Akin E,Ozer A B.Chaos embedded particle swarm optimization algorithms[J].Chaos,Solitons & Fractals,2009,40(4):1715-1734
[10]	Caponetto R,Fortuna L,Fazzino S,et al.Chaotic sequences to improve the performance of evolutionary algorithms[J].Evolutionary Computation,2003,7(3):289-304
[11]	雷德明,严新平,吴智铭.多目标混沌进化算法[J].电子学报,2006,34(6):1142-1145 Lei Deming,Yan Xinping,Wu Zhiming.Multi-objective chaotic evolutionary algorithm[J].Acta Electronica Sinica,2006,34(6):1142-1145(in Chinese)
[12]	单梁,强浩,李军,等.基于Tent映射的混沌优化算法[J].控制与决策,2005,20(2):179-182 Shan Liang,Qiang Hao,Li Jun,et al.Chaotic optimization algorithm based on Tent map[J].Control and Decision,2005,20(2):179-182(in Chinese)
[13]	Tavazoei M S,Haeri M.Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms[J].Applied Mathematics and Computation,2007,187(2):1076-1085
[14]	张浩,张铁男,沈继红,等.Tent混沌粒子群算法及其在结构优化决策中的应用[J].控制与决策,2008,23(8):857-862 Zhang Hao,Zhang Tienan,Shen Jihong,et al.Research on decision-makings of structure optimization based on improved Tent PSO[J].Control and Decision,2008,23(8):857-862(in Chinese)
[15]	程志刚,张立庆,李小林,等.基于Tent映射的混沌混合粒子群优化算法[J].系统工程与电子技术,2007,29(1):103-106 Cheng Zhigang,Zhang Liqing,Li Xiaolin,et al.Chaotic hybrid particle swarm optimization algorithm based on Tent map[J].Systems Engineering and Elect ronics,2007,29(1):103-106(in Chinese)
[16]	高鹰,谢胜利.混沌粒子群优化算法[J].计算机科学,2004,31(8):13-15 Gao Ying,Xie Shengli.Chaos particle swarm optimization algorithm[J].Computer Science,2004,31(8):13-15 (in Chinese)
[17]	盛煜翔,潘海天,夏陆岳,等.混合混沌粒子群算法在苯与甲苯闪蒸过程优化中的应用[J].浙江工业大学学报,2010,38(3):318-321 Sheng Yuxiang,Pan Haitian,Xia Luyue,et al.Hybrid chaos particle swarm optimization algorithm and application in benzene-toluene flash vaporization[J].Journal of Zhejiang University of Technology,2010,38(3):318-321(in Chinese)
[18]	Lynch S.Dynamical systems with applications using MATLAB[M].Boston:Birkhäuser Boston Product,2004:69-83
[19]	雷德明,严新平.多目标智能优化算法及其应用[M].北京:科学出版社,2009:389-390 Lei Deming,Yan Xinping.Multi-objective evolutionary optimization and application[M].Beijing:Science Press,2009:389-390(in Chinese)