An Equivalent Penalty Coefficient Method: An Adaptive Penalty Approach for Population-Based Constrained Optimization
An Equivalent Penalty Coefficient Method: An Adaptive Penalty Approach for Population-Based Constrained Optimization
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DOI：
10.1109/cec.2019.8790360
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
T. Takahama;S. Sakai
中科院分区：
文献类型：
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作者：
T. Takahama;S. Sakai
罚函数法已广泛应用于求解约束优化问题。该方法将目标值与约束违例数相加并以惩罚系数加权得到的扩展目标函数进行优化。然而，由于每个问题对系数的适当控制是不同的，因此很难对系数进行适当的控制。本文提出了基于种群的优化算法的等效惩罚系数值（EPC）。EPC可以在poa中定义，其中将新解决方案与旧解决方案进行比较。EPC是使两个解的扩展目标值相同的惩罚系数值。通过选取较小的EPC，实现了以目标值优先的搜索。通过选择一个较大的EPC来实现对约束违反的优先搜索。通过选择合适的消失模，可以实现惩罚系数的自适应控制。将该方法引入到微分进化中，并通过求解著名的约束优化问题证明了该方法的优越性。
The penalty function method has been widely used for solving constrained optimization problems. In the method, an extended objective function, which is the sum of the objective value and the constraint violation weighted by the penalty coeffi-cient, is optimized. However, it is difficult to control the coefficient properly because proper control of the coefficient varies in each problem. In this study, the equivalent penalty coefficient value (EPC) is proposed for population-based optimization algorithms (POAs). EPC can be defined in POAs where a new solution is compared with the old solution. EPC is the penalty coefficient value that makes the two extended objective values of the solutions the same. Search that gives priority to the objective value is realized by selecting a small EPC. Search that gives priority to the constraint violation is realized by selecting a large EPC. The adaptive control of the penalty coefficient can be realized by selecting an appropriate EPC. The proposed method is introduced to differential evolution and the advantage of the proposed method is shown by solving well-known constrained optimization problems.