您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（理学版）》

山东大学学报（理学版） ›› 2017, Vol. 52 ›› Issue (11): 1-10.doi: 10.6040/j.issn.1671-9352.0.2017.193

• •    下一篇

基于个体强度的自适应差分多目标免疫算法

史佩昀,高兴宝*   

  1. 陕西师范大学数学与信息科学学院, 陕西 西安 710119
  • 收稿日期:2017-04-21 出版日期:2017-11-20 发布日期:2017-11-17
  • 通讯作者: 高兴宝(1966— ),男,博士,教授,研究方向为智能优化方法、最优化理论与算法. E-mail:xinbaog@snnu.edu.cn E-mail:151569@snnu.edu.cn
  • 作者简介:史佩昀(1992— ),女,硕士研究生,研究方向为智能优化算法. E-mail:151569@snnu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61273311)

Individual strength-based multi-objective immune algorithm with adaptive differential evolution

SHI Pei-yun, GAO Xing-bao*   

  1. School of Mathematics and Information Science, Shaanxi Normal University, Xian 710119, Shaanxi, China
  • Received:2017-04-21 Online:2017-11-20 Published:2017-11-17

PDF (PC)

605

摘要： 考虑到支配解可能携带有利于算法搜索到最优解的信息, 在克隆阶段选择一部分非支配解和支配解克隆以提高种群多样性和避免算法早熟收敛。在进化阶段, 先采用自适应差分进化算子交叉变异, 然后用多项式变异算子进行扰动以有效地平衡算法的全局搜索和局部搜索。基于个体强度建立外部文档储存一定数量的较好解, 并让这些较好解在每次迭代中参与进化且被更新。对10个标准测试函数进行仿真实验, 并与其他5种算法进行比较, 结果表明所提算法在收敛性和解的分布性方面均表现出明显优势。

关键词: 多目标优化, 个体强度, 免疫算法, 差分进化

Abstract: Considering that some information contained in the dominant solution may be helpful to search for the optimal solution, some nondominated solutions and dominated solutions are selected in the clone phase to enhance the diversity of population and avoid the premature. In the evolutionary phase, crossover and mutation are excuted by an adaptive differential evolution operator and population is perturbed by the polynomial mutation operator to balance effectively global and local search of the algorithm. An archive is built based on individual strength to store a number of good solutions which are evolved and updated at each iteration. The proposed algorithm is compared with five existing evolutionary algorithms on ten standard benchmark functions. Experimental results show that the proposed algorithm has superiority in convergence and distribution.

Key words: multi-objective optimization, immune algorithm, individual strength, differential evolution

中图分类号: 

  • TP301
[1] DEB K, Multiobjective optimization using evolutionary algorithms[M]. New York: Wiley, 2001.
[2] 王宇平. 进化计算的理论与方法[M]. 北京: 科学出版社, 2010. WANG Yuping. Theory and method of evolutionary computation[M]. Beijing: Science Press, 2010.
[3] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.
[4] ZITZLER E, LAUMANNS M, THIELE L. SPEA2: improving the strength Pareto evolutionary algorithm for multiobjective optimization[M]. Berlin: Springer-Verlag, 2002: 95-100.
[5] ZHANG Qingfu, LI Hui. MOEA/D: a multiobjective evolutionary algorithm based on decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6):712-731.
[6] 谢承望, 李凯, 廖国勇. 一种带差分局部搜索的改进型NAGA2算法[J]. 计算机科学, 2013, 40(10): 235-273. XIE Chengwang, LI Kai, LIAO Guoyong. An improved NSGA2 with differential local search[J]. Computer Science, 2013, 40(10):235-273.
[7] 翁理国, 王安, 夏旻,等. 基于局部搜索的改进SPEA2算法[J]. 计算机应用研究, 2014, 31(9):2617-2639. WENG Liguo, WANG An, XIA Min, et al. An improved SPEA2 based on local search[J]. Application Research of Computers, 2014, 31(9):2617-2639.
[8] 谭艳艳. 几种改进的分解类多目标进化算法及其应用[D]. 西安: 西安电子科技大学, 2013. TAN Yanyan. Several improved multi-objective evolutionary algorithms based on decomposition and its application[D]. Xian: Xidian University, 2013.
[9] DE CASTRO L N, TIMMIS J. Artificial immune systems: a new computational intelligence approach[M]. London: Springer, 2002.
[10] GONG Maoguo, JIAO Licheng, DU Haifeng, et al. Multi-objective immune algorithm with nondominated neighbor-based selection[J]. IEEE Transactions on Evolutionary Computation, 2008, 16(2):225-255.
[11] LIN Qiuzhen, ZHU Qingling, HUANG Peizhi, et al. A novel hybrid multi-objective immune algorithm with adaptive differential evolution[J]. Computers & Operations Research, 2015, 62:95-111.
[12] LIN Qiuzhen, CHEN Jianyong, ZHAN Zhihui, et al. A hybrid evolutionary immune algorithm for multiobjective optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(5):711-729.
[13] YOO J, HAJELA P. Immune network simulations in multicriterion design[J]. Structural and Multidisciplinary Optimization, 1999, 18(2-3):85-94.
[14] BOSMAN P A N, THIERENS D. The balance between proximity and diversity in multiobjective evolutionary algorithms[J]. IEEE Transactions on Evolutionary Computation, 2003, 7(2):174-188.
[15] 张岭军.基于多目标免疫算法的动态网络社区测试[D].西安:西安电子科技大学, 2012. ZHANG Lingjun. Dynamic network community test based on multi-objective immune algorithm[D]. Xian: Xidian University, 2012.
[16] QIN A K, HUANG V L, SUGANTHAN P N. Differential evolution algorithm with strategy adaptation for numerical optimization[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(2):398-417.
[17] Bilal Alatas. Chaotic bee colony algorithms for global numerical optimization[J]. Expert Systems with Applications, 2010, 37(8):5682-5687.
[18] SABAT S L, KUMAR K S, UDGATA S K. Differential evolution and swarm intelligence techniques for analog circuit synthesis[C] // Proceedings of 2009 World Congress on Nature & Biologically Inspired Computing. New York: IEEE, 2009: 469-474.
[19] 王安. SPEA2算法改进在机器人路径规划中的应用[D]. 南京: 南京信息工程大学, 2014. WANG An. Application of SPEA2 algorithm in robot path planning[D]. Nanjing: Nanjing University of Information Science & Technology, 2014.
[20] ZITZLER E, DEB K, THIELE L. Comparison of multiobjective evolutionary algorithms: empirical results[J]. Evolutionary Computation, 2000, 8(2):173-195.
[21] DEB K, THIELE L, LAUMANNS M, et al. Scalable multi-objective optimization test problems[C] // Proceedings of the World on Congress on Computational Intelligence, 2002: 825-830.
[22] YANG Dongdong, JIAO Licheng, GONG Maoguo. Adaptive multi-objective optimization based on nondominated solutions[J]. Computational Intelligence, 2009, 25(2):84-108.
[23] LI Hui, ZHANG Qingfu. Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(2):284-302.
[1] 吴迪,李苏剑,李海涛. 基于时间满意度的混合渠道库存及分配策略[J]. J4, 2013, 48(6): 51-60.
[2] 吕小妮1,王艳彩2,高岳林2. BVaR风险度量下限制性卖空的单位风险收益最大投资组合模型[J]. J4, 2013, 48(05): 92-96.
[3] 裴胜玉，周永权*. 一种基于混沌变异的多目标粒子群优化算法[J]. J4, 2010, 45(7): 18-23.
[4] 李彬1,2,李贻斌1,荣学文1. ELM-RBF神经网络的智能优化策略[J]. J4, 2010, 45(5): 48-51.
[5] 郑向伟,刘 弘 . 一种多样性引导的两阶段多目标微粒群算法[J]. J4, 2008, 43(11): 5-10 .
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(理学版)》编辑部　
地址: 山东省济南市山大南路27号山东大学中心校区（250100）　电话: +86-0531-88366917　E-mail: xblxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发