Multiobjective immune algorithm with nondominated neighbor-based selection
Multiobjective immune algorithm with nondominated neighbor-based selection
复制标题
基于非支配邻居选择的多目标免疫算法
DOI：
10.1162/evco.2008.16.2.225
复制
发表时间：
2008-06-01
影响因子：
6.8
通讯作者：
Bo, Liefeng
中科院分区：
文献类型：
--
作者：
Gong, Maoguo;Jiao, Licheng;Bo, Liefeng
提出了一种基于非支配邻域选择技术、一种免疫启发算子、两种启发式搜索算子和精英策略的非支配邻域免疫算法（NNIA）。NNIA的独特选择技术只选择群体中少数孤立的非优势个体。然后，在启发式搜索之前，所选择的个体与其拥挤距离值成比例地克隆。NNIA通过使用基于非支配邻居的选择和比例克隆，更加关注当前权衡前沿的不那么拥挤的区域。我们比较NNIA与NSGA-II，SPEA 2，PESA-II，和MISA在解决五个DTLZ问题，五个ZDT问题，和三个低维问题。基于两个集合的覆盖率、收敛性度量和间隔三个性能度量的统计分析表明，唯一选择方法是有效的，NNIA是一种求解多目标优化问题的有效算法. NNIA的可扩展性方面的目标数量的实证研究表明，新算法的规模以及沿着目标的数量。
Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.