基于动态拥挤距离的混合多目标免疫优化算法  

Dynamic Crowding Distance-based Hybrid Immune Algorithm for Multi-objective Optimization Problem

作　　者：马元锋[1] 李昂儒 余慧敏 潘晓英 MA Yuan- feng1, LI Ang -ru2, YU Hui -min2 ,PAN Xiao- ying2(1The Third Research Institute of China Electronic Technology Group Corporation,Beijing 100015 ,China;2School of Computer Science and Technology, Xi＇an University of Posts and Telecommunications, Xi＇an 710121, China;3 Key Laboratory of Network Data Intelligent Processing, Xi＇ an University of Posts and Telecommunications,Xi＇an 710121, Chin)

机构地区：[1]中国电子科技集团公司第三研究所,北京100015 [2]西安邮电大学计算机学院,西安710121 [3]西安邮电大学陕西省网络数据智能处理重点实验室,西安710121

出　　处：《计算机科学》2018年第B06期63-68,共6页Computer Science

基　　金：国家自然科学基金青年项目(61203311)资助

摘　　要：多目标免疫优化算法的研究目标是种群均匀分布于优化问题的非劣最优域并使算法快速收敛。为进一步提高多目标优化问题非支配解集合的分布均匀性和收敛性,提出了一种基于动态拥挤距离的混合多目标免疫优化算法。该算法基于动态拥挤距离来对个体进行比较和更新操作,从而保持最终解集的均匀分布,同时借鉴经典差分进化算法中的变异引导算子来加强免疫优化算法的局部搜索能力并提高搜索精度。基于5个经典测试函数的仿真结果表明,与其他几种有效的多目标优化算法相比,所提算法不仅在求得Pareto最优解集的逼近性、均匀性和宽广性上有明显优势,而且收敛速度也有较大的改进和提高。The goal of the research on multi-objective immune optimization algorithm is to make the population uniformly distributed in Pareto optimal domain and make the algorithm converge fast.To improve the diversity and convergence of the non-dominated solution set,a dynamic crowding distance-based hybrid immune algorithm for multi-objective optimization problem was presented in this paper.The algorithm uses dynamic crowding distance calculation to compare and update individuals in each subpopulation.Meanwhile,it references mutation-guiding operator of differential evolution to strengthen the local search ability and improve search precision of the immune optimization algorithm.Compared with the other three efficient multi-objective optimization algorithms,five benchmark test problems and simulation results indicate that the algorithm performs better in approximation,uniformity and coverage.It converges significantly faster than the relevant optimization algorithms.

关 键 词：多目标优化 人工免疫算法 动态拥挤距离 差分算子 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]