Constraint-Handling Method for Multi-objective Function Optimization: Pareto Descent Repair Operator
Constraint-Handling Method for Multi-objective Function Optimization: Pareto Descent Repair Operator
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DOI：
10.1007/978-3-540-70928-2_15
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发表时间：
2007-03
期刊：
影响因子：
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通讯作者：
Ken Harada;J. Sakuma;I. Ono;S. Kobayashi
中科院分区：
文献类型：
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作者：
Ken Harada;J. Sakuma;I. Ono;S. Kobayashi
在迄今为止提出的多目标优化方法中，遗传算法（GA）在最近几十年中被证明是更有效的。大多数这样的方法主要是用来解决无约束问题的。然而，许多现实世界的问题是受约束的，这就需要适当地处理约束。尽管人们对约束处理方法进行了大量的研究，但据报道，每种方法都有一定的局限性。因此，需要进一步研究设计更有效的约束处理方法。在此基础上，结合多目标局部搜索和梯度投影的思想，设计了一种新的约束处理方法--Pareto下降修复算子（PDR）。最后通过实验将PDR算法与现有的约束处理方法进行了比较，验证了PDR算法的有效性。
Among the multi-objective optimization methods proposed so far, Genetic Algorithms (GA) have been shown to be more effective in recent decades. Most of such methods were developed to solve primarily unconstrained problems. However, many real-world problems are constrained, which necessitates appropriate handling of constraints. Despite much effort devoted to the studies of constraint-handling methods, it has been reported that each of them has certain limitations. Hence, further studies for designing more effective constraint-handling methods are needed.For this reason, we investigated the guidelines for a method to effectively handle constraints. Based on these guidelines, we designed a new constraint-handling method, Pareto Descent Repair operator (PDR), in which ideas derived from multi-objective local search and gradient projection method are incorporated. An experiment comparing GA that use PDR and some of the existing constraint-handling methods confirmed the effectiveness of PDR.