Adaptively Allocating Search Effort in Challenging Many-Objective Optimization Problems
Adaptively Allocating Search Effort in Challenging Many-Objective Optimization Problems
复制标题
自适应分配搜索工作量以应对多目标优化问题
DOI：
10.1109/tevc.2017.2725902
复制
发表时间：
2018-06
期刊：
影响因子：
--
通讯作者：
Kdeb
中科院分区：
文献类型：
--
作者：
Hai-Lin Liu;Lei Chen;Qingfu Zhang;Kdeb
在多目标优化问题中，特别是在多目标优化问题中，有效分配搜索量是非常重要的。本文提出了一种基于分解MOEA/D- m2m的多目标进化算法自适应搜索力分配策略。该方法通过自适应检测不同目标的重要性，对子问题的子区域进行自适应调整。更具体地说，它根据当前解在目标空间中的分布周期性地重置子区域设置，这样搜索工作就不会浪费在没有希望的区域上。其基本思想是，当前种群可以被视为帕累托前沿（PF）的近似值，因此可以隐式估计PF的形状，并且可以使用这种估计来调整搜索焦点。通过与8种具有代表性的竞争算法在一组具有连通和不连通PFs的退化MaOPs上进行比较，验证了该算法的性能。研究了该算法在具有连通和不连通PFs的非退化测试实例上的性能。
An effective allocation of search effort is important in multiobjective optimization, particularly in many-objective optimization problems (MaOPs). This paper presents a new adaptive search effort allocation strategy for multiobjective evolutionary algorithm based on decomposition MOEA/D-M2M, a recent MOEA/D algorithm for challenging MaOPs. This proposed method adaptively adjusts the subregions of its subproblems by detecting the importance of different objectives in an adaptive manner. More specifically, it periodically resets the subregion setting based on the distribution of the current solutions in the objective space such that the search effort is not wasted on unpromising regions. The basic idea is that the current population can be regarded as an approximation to the Pareto front (PF) and thus one can implicitly estimate the shape of the PF and such estimation can be used for adjusting the search focus. The performance of proposed algorithm has been verified by comparing it with eight representative and competitive algorithms on a set of degenerated MaOPs with disconnected and connected PFs. Performances of the proposed algorithm on a number of nondegenerated test instances with connected and disconnected PFs are also studied.
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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：
10.1007/11844297_54
发表时间：
2006-09
期刊：
--
影响因子：
--
作者：
D. Brockhoff;E. Zitzler
通讯作者：
D. Brockhoff;E. Zitzler
影响因子：
14.3
作者：
Jain, Himanshu;Deb, Kalyanmoy
通讯作者：
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影响因子：
14.3
作者：
D. Saxena;João A. Duro;A. Tiwari;K. Deb;Qingfu Zhang
通讯作者：
D. Saxena;João A. Duro;A. Tiwari;K. Deb;Qingfu Zhang
影响因子：
8.8
作者：
D. Saxena;K. Deb
通讯作者：
D. Saxena;K. Deb