WBMOAIS: A novel artificial immune system for multiobjective optimization
WBMOAIS: A novel artificial immune system for multiobjective optimization
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DOI：
10.1016/j.cor.2009.03.009
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
Jiaquan Gao;Jun Wang
中科院分区：
文献类型：
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作者：
Jiaquan Gao;Jun Wang
本研究提出了一种基于 opt-aiNET 的新型基于权重的多目标人工免疫系统 (WBMOAIS)，这是一种用于多模态优化的人工免疫系统算法。该算法遵循opt-aiNET的基本结构，但具有以下明显特征：（1）使用多个目标的随机加权和作为适应度函数。适应度分配的计算复杂度比基于 Pareto 排序的计算复杂度低得多，（2）从内存中选择种群中的个体，这是一组精英解决方案，并利用局部搜索程序来促进搜索空间的开发，（3）除了类似于 opt-aiNET 中使用的克隆抑制算法之外，还提出了一种新的相似个体截断算法（TASI），以消除内存中的相似个体并获得均匀分布的非支配分布。解决方案。将所提出的 WBMOAIS 算法与向量免疫算法 (VIS) 和精英非支配排序遗传系统 (NSGA-II) 进行比较，这些算法代表了多目标优化元启发法的最新技术。对七个标准问题（ZDT6、SCH2、DEB、KUR、POL、FON 和 VNT）的仿真结果表明，WBMOAIS 的性能优于 VIS 和 NSGA-II，可以成为解决多目标优化问题的标准算法的有效替代方案。
This study presents a novel weight-based multiobjective artificial immune system (WBMOAIS) based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. The proposed algorithm follows the elementary structure of opt-aiNET, but has the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function. The fitness assignment has a much lower computational complexity than that based on Pareto ranking, (2) the individuals of the population are chosen from the memory, which is a set of elite solutions, and a local search procedure is utilized to facilitate the exploitation of the search space, and (3) in addition to the clonal suppression algorithm similar to that used in opt-aiNET, a new truncation algorithm with similar individuals (TASI) is presented in order to eliminate similar individuals in memory and obtain a well-distributed spread of non-dominated solutions. The proposed algorithm, WBMOAIS, is compared with the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II) that are representative of the state-of-the-art in multiobjective optimization metaheuristics. Simulation results on seven standard problems (ZDT6, SCH2, DEB, KUR, POL, FON, and VNT) show WBMOAIS outperforms VIS and NSGA-II and can become a valid alternative to standard algorithms for solving multiobjective optimization problems.