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于 2011-01-20 发布文件大小:102KB

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遗传算法是模拟达尔文的遗传选择和自然淘汰的生物进化过程的计算模型。它的思想源于生物遗传学和适者生存的自然规律，是具有“生存＋检测”的迭代过程的搜索算法。遗传算法以一种群体中的所有个体为对象，并利用随机化技术指导对一个被编码的参数空间进行高效搜索。(Genetic algorithm is simulated Darwinian natural selection of genetic selection and evolution process model. It' s thought from biological genetics and survival of the fittest laws of nature, is a " survival+ detection" iterative process of search algorithm. Genetic algorithms in a group all the individuals targeted, and technical guidance on the use of a randomized coded efficiently search parameter space.)

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本文二次更新型粒子群算法（ TUPSO）的基本思想是：一方面，在保留粒子群算法中快速收敛，易于实现，调整参数少等优势的前提下，通过融合基本粒子群算法中的信息共享原理与 DPSO-TSP 中的 2-Opt 运算法则，改进了DPSO-TSP中粒子进化的规则(In this paper, the basic idea of the new particle swarm optimization (TUPSO) algorithm is: on the one hand, by integrating the particle swarm optimization algorithm in the fast convergence, easy to achieve, adjust the parameters of less advantage of the premise, through the integration of basic particle swarm algorithm information sharing Principle and the 2-Opt algorithm in DPSO-TSP improves the rule of particle evolution in DPSO-TSP)

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2012-12-28 17:57:55下载

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