A two-archive algorithm with decomposition and fitness allocation for multi-modal multi-objective optimization
A two-archive algorithm with decomposition and fitness allocation for multi-modal multi-objective optimization
复制标题
一种具有分解和适应度分配的多模态多目标优化二档案算法
DOI：
10.1016/j.ins.2021.05.075
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发表时间：
2021-06
影响因子：
8.1
通讯作者：
Zheng Jinhua
中科院分区：
文献类型：
--
作者：
Li Zhipan;Zou Juan;Yang Shengxiang;Zheng Jinhua
针对同一目标向量对应一个以上Pareto最优解集的多模式多目标优化问题，提出了一种具有分解和适应度分配的两档算法。该方法的总体框架使用两个档案库，收敛档案库(CA)和多样性档案库(DA)，它们分别关注种群的收敛和多样性。这两个档案都是基于分解的框架。在蚁群算法中，种群更新策略采用了一种适应度方案，该方案根据种群在进化过程中的变化状态进行设计，将目标空间的收敛和决策空间的多样性结合起来。在DA中，我们使用拥挤距离策略来保证决策空间的多样性。此外，使用不同的邻域准则来保证两个档案馆的种群的收敛和多样性。结果表明，该算法不仅能够找到并保持大量的Pareto最优集，而且在决策空间和目标空间都能获得良好的多样性和收敛性能。此外，在两组测试函数上，将该算法与现有的五种进化算法进行了实证比较。比较结果表明，该算法具有较好的性能。
This paper proposes a two-archive algorithm with decomposition and fitness allocation for multi-modal multi-objective optimization problems which have more than one Pareto-optimal solution set corresponding to the same objective vector. The general framework of the proposed method uses two archives, the convergence archive (CA) and the diversity archive (DA), which focus on the convergence and diversity of population, respectively. Both archives are based on a decomposition-based framework. In CA, the population update strategy adopts a fitness scheme, which is designed according to the change state of population during evolution, combining the convergence of the objective space with the diversity of the decision space. In DA, we use the crowding distance strategy to ensure the diversity of the decision space. Moreover, different neighborhood criteria are used to ensure the convergence and diversity of population for two archives. The algorithm is shown to not only locate and maintain a larger number of Pareto-optimal sets, but also to obtain good diversity and convergence in both the decision and objective spaces. In addition, the proposed algorithm is empirically compared with five state-of-the-art evolutionary algorithms on two series of test functions. Comparison results show that the proposed algorithm has better performance than the competing algorithms.
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影响因子：
14.3
作者：
Wang, Handing;Jiao, Licheng;Yao, Xin
通讯作者：
Yao, Xin
DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
--
作者：
A. Engelbrecht
通讯作者：
A. Engelbrecht
DOI：
10.1016/b978-0-12-409547-2.14581-0
发表时间：
2020
期刊：
Comprehensive Chemometrics
影响因子：
--
作者：
Federico Marini;Beata Walczak
通讯作者：
Federico Marini;Beata Walczak
进化多模态多目标优化中决策空间收敛性与多样性不平衡的处理
影响因子：
14.3
作者：
Yiping Liu;Hisao Ishibuchi;Gary G. Yen;Yusuke Nojima;Naoki Masuyama
通讯作者：
Naoki Masuyama
DOI：
10.1201/9781003206477-5
发表时间：
2021-08
期刊：
Evolutionary Optimization Algorithms
影响因子：
--
作者：
A. Badar
通讯作者：
A. Badar