A modified objective function method with feasible-guiding strategy to solve constrained multi-objective optimization problems
A modified objective function method with feasible-guiding strategy to solve constrained multi-objective optimization problems
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DOI：
10.1016/j.asoc.2013.10.008
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
L. Jiao;Juanjuan Luo;Ronghua Shang;Fang Liu
中科院分区：
文献类型：
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作者：
L. Jiao;Juanjuan Luo;Ronghua Shang;Fang Liu
在约束多目标优化问题中，如何保留不可行个体并加以利用是一个亟待解决的问题。在这种情况下，本文提出了一种改进的目标函数方法与可行的指导策略的基础上NSGA-II处理CMOPs。该算法的主要思想是用个体的约束违反值和真实目标函数值来修正个体的目标函数值，其中利用当前种群反馈的可行性比来保持平衡，然后采用可行性引导策略来利用保留的不可行个体。这样，该算法获得的非支配解在收敛性和分布多样性方面表现出优越性，这一点可以通过与其他两种CMOEA在常用约束测试问题上的比较实验结果得到证实。
For constrained multi-objective optimization problems (CMOPs), how to preserve infeasible individuals and make use of them is a problem to be solved. In this case, a modified objective function method with feasible-guiding strategy on the basis of NSGA-II is proposed to handle CMOPs in this paper. The main idea of proposed algorithm is to modify the objective function values of an individual with its constraint violation values and true objective function values, of which a feasibility ratio fed back from current population is used to keep the balance, and then the feasible-guiding strategy is adopted to make use of preserved infeasible individuals. In this way, non-dominated solutions, obtained from proposed algorithm, show superiority on convergence and diversity of distribution, which can be confirmed by the comparison experiment results with other two CMOEAs on commonly used constrained test problems.