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中文摘要：
求解动态多目标优化（dynamic multi—objective optimization，简称DMO）问题的主要困难在于目标函数、约束条件或者相关的问题参数是随时间不断变化的．基于免疫克隆选择学说，提出一种用于解决DMO问题的新算法——动态多目标免疫克隆优化（immune clonal algorithm for DMO，简称ICADMO）．该算法改进了现有的克隆策略，采用整体克隆的方式；在选择策略上l艮据Pareto-占优的概念，将抗体群中的个体分为支配个体和非支配个体，对非支配个体进行选择．采用3个特色算子，使其很好地保持了所得解的多样性、均匀性和收敛性．通过数值实验，与DBM（direction—based method）算法进行比较，结果表明，新算法在收敛性、多样性以及解分布的广度方面都体现了很好的性能.
英文摘要：
The difficulty of Dynamic Multi-Objective Optimization （DMO） problem lies in either the objective function and constraint or the associated problem parameters variation with time. In this paper, based on the immune clonal theory, a new DMO algorithm termed as Immune Clonal Algorithm for DMO （ICADMO） is proposed. In the algorithm, the entire cloning is adopted and the clonal selection based on the Pareto-dominance is adopted. The individuals in the antibody population are divided into two parts： Dominated ones and non-dominated ones, and the non-dominated ones are selected. Three operators are introduced into ICADMO, which guarantees the diversity, the uniformity and the convergence of the obtained solutions. ICADMO is tested on four DMO test problems and compared with the Direction-Based Method （DBM）, and much better performance in both the convergence and diversity of the obtained solutions is observed.
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An asynchronous parallel evolutionary algorithm (APEA) for solving complex non-linear real world opt
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