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A dynamic particle swarm optimizer for educational data processing

机译:用于教育数据处理的动态粒子群优化器

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Many problems in educational data processing on Internet are dynamic optimization problems. These DOPs challenge traditional optimization methods as well as conventional evolutionary algorithms. In this paper, an improved Particle Swarm Optimizer (PSO) by introducing Group Search Optimizer (GSO) mechanisms is proposed to solve optimization in dynamic environment. The member switch method between two roles in GSO is employed as immigrant method among multi-swarms. Experimental results on unimodal and multimodal benchmarks demonstrate the proposed algorithm can trace the moving optimal and are effective in dynamic environment.
机译:互联网上的教育数据处理中的许多问题是动态优化问题。这些DOPS挑战传统的优化方法以及传统的进化算法。本文通过引入组搜索优化器(GSO)机制,提出了一种改进的粒子群优化器(PSO),以解决动态环境中的优化。 GSO中两个角色之间的成员切换方法是多群中的移民方法。在单向和多模式基准测试上的实验结果证明了所提出的算法可以追踪移动最佳的并且在动态环境中有效。

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