基于人工免疫的多目标优化研究综述

Overview of multi-objective optimizations based on artificial immune

免疫启发的多目标优化算法近来逐渐受到研究者的关注,免疫系统由于其固有优点成为继进化算法之后又一个成功的启发式搜索策略研究方向。首先给出了多目标优化和免疫算法的基础概念和常用术语,深入分析了现有的各种基于免疫网络的免疫优化算法和基于克隆选择的免疫优化算法,阐述了算法的特点及改进,重点描述了免疫优化算法在实际应用中的研究;在介绍了免疫优化算法的收敛性理论证明和免疫优化算法的评价方法之后,阐述了免疫优化算法目前的研究热点和趋势。

Immune-based heuristic search algorithms are gaining more and more attention from researchers. Immune algorithms have become another successful heuristic search research field because of its inner good characteristics. This paper first introduced the basics and constantly used terms of multi-objective optimizations and artificial immune systems, and then illustrated existing famous immune optimization algorithms, which respectively based on immune network and elonal selection. Especially it concentrated on the application of immune algorithms. After providing theoretical ways of proving the convergence of immune optimization algorithms, and giving several frequently cited comments and quality indicators for immune optimization algorithms evaluation, gave the hot-spot of immune optimization and future research trends.