Gong Maoguo, Jiao Licheng, Du Haifeng, Bo Liefeng
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, China.
Evol Comput. 2008 Summer;16(2):225-55. doi: 10.1162/evco.2008.16.2.225.
Abstract 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.
摘要 提出了非支配邻域免疫算法（NNIA）用于多目标优化，该算法采用了一种基于非支配邻域的新型选择技术、一种免疫启发算子、两种启发式搜索算子以及精英保留策略。NNIA独特的选择技术仅在种群中选择少数孤立的非支配个体。然后，在启发式搜索之前，根据所选个体的拥挤距离值按比例进行克隆。通过使用基于非支配邻域的选择和比例克隆，NNIA更加关注当前权衡前沿中拥挤程度较低的区域。我们将NNIA与NSGA-II、SPEA2、PESA-II和MISA在求解五个DTLZ问题、五个ZDT问题以及三个低维问题时进行了比较。基于包括两个集合的覆盖率、收敛度量和间距这三个性能指标的统计分析表明，这种独特的选择方法是有效的，并且NNIA是一种求解多目标优化问题的有效算法。关于NNIA相对于目标数量的可扩展性的实证研究表明，新算法在目标数量增加时具有良好的扩展性。
