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Discrete Particle Swarm Optimization Based On Estimation Of Distribution For Polygonal Approximation Problems

机译：基于分布估计的离散粒子群优化算法

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摘要

The polygonal approximation is an important topic in the area of pattern recognition, computer graphics and computer vision. This paper presents a novel discrete particle swarm optimization algorithm based on estimation of distribution (DPSO-EDA), for two types of polygonal approximation problems. Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The DPSO-EDA incorporates the global statistical information collected from local best solution of all particles into the particle swarm optimization and therefore each particle has comprehensive learning and search ability. Further, constraint handling methods based on the split-and-merge local search is introduced to satisfy the constraints of the two types of problems. Simulation results on several benchmark problems show that the DPSO-EDA is better than previous methods such as genetic algorithm, tabu search, particle swarm optimization, and ant colony optimization.
机译：多边形逼近是模式识别，计算机图形学和计算机视觉领域中的重要主题。针对两种类型的多边形逼近问题，本文提出了一种基于分布估计的新型离散粒子群优化算法（DPSO-EDA）。分布算法的估计从概率模型中采样新的解决方案，该模型表征了每一代搜索空间中有希望的解决方案的分布。 DPSO-EDA将从所有粒子的局部最佳解中收集的全局统计信息整合到粒子群优化中，因此每个粒子都具有全面的学习和搜索能力。此外，引入了基于拆分合并局部搜索的约束处理方法来满足两种类型问题的约束。对几个基准问题的仿真结果表明，DPSO-EDA优于以前的方法，如遗传算法，禁忌搜索，粒子群优化和蚁群优化。

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