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mcmthesis-6.2

于 2019-01-27 发布文件大小:99KB

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代码说明：

美赛论文模板包含了摘要页，目录等，只需编辑内容即可(Thesis Template of American University Mathematical Modeling Contest)

文件列表：

mcmthesis-6.2, 0 , 2016-01-29
mcmthesis-6.2\.gitignore, 1214 , 2016-01-29
mcmthesis-6.2\LICENSE, 220 , 2016-01-29
mcmthesis-6.2\README, 1469 , 2016-01-29
mcmthesis-6.2\adjust_checksum.pl, 5287 , 2016-01-29
mcmthesis-6.2\code, 0 , 2016-01-29
mcmthesis-6.2\code\mcmthesis-matlab1.m, 196 , 2016-01-29
mcmthesis-6.2\code\mcmthesis-sudoku.CPP, 805 , 2016-01-29
mcmthesis-6.2\figures, 0 , 2016-01-29
mcmthesis-6.2\figures\mcmthesis-aaa.eps, 458067 , 2016-01-29
mcmthesis-6.2\figures\mcmthesis-logo.pdf, 3922 , 2016-01-29
mcmthesis-6.2\make, 339 , 2016-01-29
mcmthesis-6.2\mcmthesis.dtx, 39979 , 2016-01-29

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说明：粒子群算法，也称粒子群优化算法或鸟群觅食算法（Particle Swarm Optimization），缩写为 PSO，是由J. Kennedy和R. C. Eberhart等开发的一种新的进化算法(Evolutionary Algorithm - EA)。PSO 算法属于进化算法的一种，和模拟退火算法相似，它也是从随机解出发，通过迭代寻找最优解，它也是通过适应度来评价解的品质，但它比遗传算法规则更为简单，它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作，它通过追随当前搜索到的最优值来寻找全局最优。这种算法以其实现容易、精度高、收敛快等优点引起了学术界的重视，并且在解决实际问题中展示了其优越性。粒子群算法是一种并行算法(Particle Swarm Optimization (PSO), also known as Particle Swarm Optimization (PSO), is a new evolutionary algorithm developed by J. Kennedy and R. C. Eberhart. PSO is a kind of evolutionary algorithm, which is similar to simulated annealing algorithm. It starts from random solution and searches for the best solution by iteration. It also evaluates the quality of solution by fitness. But it is simpler than genetic algorithm rules. It has no Crossover and Mutation operation of genetic algorithm. As a result, it searches for global optimum by following the optimal value currently searched. This algorithm has attracted the attention of academia for its advantages of easy implementation, high accuracy and fast convergence, and has shown its superiority in solving practical problems. Particle swarm optimization is a parallel algorithm)

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