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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

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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

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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
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