约束多目标优化的模拟退火算法

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI:10.1109/CEC.2008.4631013

H. Singh, A. Isaacs, T. Ray, W. Smith

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引用次数: 19

摘要

本文介绍了一种约束多目标优化(MOO)的模拟退火算法。在可行区域搜索时，该算法的行为类似于最近提出的存档多目标模拟退火(AMOSA)算法[1]，而在不可行区域搜索时，它通过沿近似下降方向(Approximate Descent Direction, ADD)[2]移动来最小化约束违反。保存在搜索过程中发现的非支配解的存档。一个新点的接受概率取决于它的可行性状态，以及它与当前点和存档点相比的统治状态。我们报告了所提出的算法在一组七个约束双目标测试问题(CTP2至CTP8)上的性能，这些问题已知会给现有的多目标算法带来困难。将当前算法与广泛应用的多目标进化算法NSGA-II进行了比较研究。

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A simulated annealing algorithm for constrained Multi-Objective Optimization

In this paper, we introduce a simulated annealing algorithm for constrained Multi-Objective Optimization (MOO). When searching in the feasible region, the algorithm behaves like recently proposed Archived Multi-Objective Simulated Annealing (AMOSA) algorithm [1], whereas when operating in the infeasible region, it tries to minimize constraint violation by moving along Approximate Descent Direction (ADD) [2]. An Archive of non-dominated solutions found during the search is maintained. The acceptance probability of a new point is determined by its feasibility status, and its domination status as compared to the current point and the points in the Archive. We report the performance of the proposed algorithm on a set of seven constrained bi-objective test problems (CTP2 to CTP8), which have been known to pose difficulties to existing multi-objective algorithms. A comparative study of current algorithm with the widely used multi-objective evolutionary algorithm NSGA-II has been included.

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来源期刊

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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