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Description: ：针对移动机器人传统路径规划算法效率不高，寻优能力差等问题，提出一种基
于神经网络和粒子群优化算法相结合的移动机器人路径规划方法．该方法利用神经网
络实现大量的并行和分布计算，发挥PSO简单、容易实现的优点，提高了路径规划的计
算效率和可靠性．仿真结果表明，这种新路径规划方法是可行且有效的．-The quality and eficiency of calculation is the two puzzling problems in the tradi—
tional algorithm for the robot path planning．In this paper，a new method of obstacle avoidance
and path planning based on neural network and particle swarm optimization is proposed．In
this method，a neural network is used to realize substantive parallel and distributed compu—
ting．And also this exerts the merit of PSO，which improves the computational eficiency and
reliability．As it is proved by analysis and test，that a better result is obtained by the pro—
posed algorithm．
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Author: 王风 |
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Description: 一种新的并行文化微粒群优化算法,为了避免微粒群优化算法在解决复杂优化问题时陷入局部最优-A new parallel culture particle swarm optimization, in order to avoid particle swarm optimization in solving complex optimization problems into local optimum
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Author: 刘欣 |
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Description: 多目标优化
相对传统多目标优化方法, PSO在求解多目标问题上具有很大优势。首先, PSO的高效搜索能力有利于得到多目标意义下的最优解 其次, PSO通过代表整个解集的种群按内在的并行方式同时搜索多个非劣解,因此容易搜索到多个Pareto 最优解 再则, PSO的通用性使其适合于处理所有类型的目标函数和约束 另外, PSO 很容易与传统方法相结合,进而提出解决特定问题的高效方法。就PSO 本身而言,为了更好地解决多目标优化问题,必须解决全局最优粒子和个体最优粒子的选择问题-Compared with the traditional multi-objective optimization of multi-objective optimization method, PSO in solving multi-objective problem has a great advantage. First, PSO is conducive to the efficient search capabilities are more objective sense of the optimal solution Secondly, PSO representative of the entire solution set through the population by way of the inherent parallel search multiple non-inferior solution, and this can easily search for the most number of Pareto optimal solution Furthermore, PSO' s versatility make it suitable for handling all types of objective function and constraints addition, PSO is easy to integrate with the traditional method, and then propose an efficient way to solve specific problems. The PSO itself, in order to better address the multi-objective optimization problems, the need to address the global best particle and the individual selection of the optimal particle
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Author: 杨科 |
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Description: 本粒子群优化算法（Particle Swarm Optimization）名称：基本粒子群优化算法（PSO）
作用：求解优化问题
说明：全局性，并行性，高效的群体智能算法
-The particle swarm optimization algorithm (Particle of Swarm Optimization) name: Particle Swarm Optimization algorithm (PSO)
Role: solving optimization problems
Description: global, parallel, efficient group of intelligent algorithm
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Size: 4096 |
Author: feilong |
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Description: 粒子群優化算法（粒子群優化）名稱：粒子群優化算法（PSO）
作用：解決優化問題
說明：全局，並行，高效的智能算法組-The particle swarm optimization algorithm (Particle of Swarm Optimization) name: Particle Swarm Optimization algorithm (PSO)
Role: solving optimization problems
Description: global, parallel, efficient group of intelligent algorithm
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Author: feilong |
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Description: 介绍了一种利用量子行为粒子群算法(QPSO)求解多峰函数优化问题的方法。为此，在
QPSO中引进一种物种形成策略，该方法根据群体微粒的相似度并行地分成子群体。每个子群体是
围绕一个群体种子而建立的。对每个子群体通过QPSO算法进行最优搜索。从而保证每个峰值都有
同等机会被找到，因此该方法具有良好的局部寻优特性。将基于物种形成的QPSO算法与粒子群算
法(PSO)对多峰优化问题的结果进行比较。对几个重要的测试函数进行仿真实验结果证明，基于物
种形成的QPSO算法可以尽可能多地找到峰值点，峰值收敛性能优于PS-A quantum behaved particle swarm optimization (QPSO) method for solving multimodal function optimization problems. For this reason, the introduction of a species in QPSO form a strategy, the method is based on the similarity of the groups of particles divided into sub-groups in parallel. Each sub-group is established around the seeds of a group. The QPSO algorithm optimal search for each sub-group. In order to ensure that each peak has the same opportunity to find, this method has good local optimization features. Will compare the results of multimodal optimization problems based on QPSO algorithm and particle swarm optimization (PSO) species formed. Simulation results prove that several important test function, based species formed QPSO algorithm can be as much as possible to find the peak point, the peak convergence outperforms PS
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Author: zhuifenger |
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Description: Parallel PSO Using MapReduce
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Author: Ethan |
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Description: mpi.net sample of tsp problem using pso algorithm
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Author: smsm |
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Description: 粒子群算法源程序，是近年来发展起来的一种新的进化算法。有实现容易、精度高、收敛快等优点。是一种并行算法。-Particle swarm algorithm source code, is a new evolutionary algorithm developed in recent years. There are easy to implement, high precision, fast convergence and so on. Is a kind of parallel algorithms.
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Author: Mickel |
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Description: 粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO， 是近年来发展起来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, also known as particle swarm optimization (Particle Swarm Optimization), abbreviated as PSO, is a new evolutionary algorithm developed in recent years (Evolutionary Algorithm- EA). Kind, and simulated annealing algorithm PSO algorithm is similar evolutionary algorithms, it is also starting a random solution, through an iterative search for the optimal solution, which is also used to uate the quality through fitness solution, but it is simpler than genetic algorithm rules It has no genetic algorithm " crossover" (Crossover) and " variant" (Mutation) operation, which by following the current search to find the optimal value to the global optimum. This algorithm is its easy implementation, high accuracy, fast convergence, etc. attracted academic attention and show its superiority in solving practical problems. PSO algorithm is a parallel algorithm.
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Author: 艾岳巍 |
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Description: 粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO， 是近年来发展起来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。
BP（Back Propagation）神经网络是1986年由Rumelhart和McCelland为首的科学家小组提出，是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hidden layer)和输出层(output layer)。-Particle swarm optimization, also known as particle swarm optimization (Particle Swarm Optimization), abbreviated as PSO, is a new evolutionary algorithm developed in recent years (Evolutionary Algorithm- EA). Kind, and simulated annealing algorithm PSO algorithm is similar evolutionary algorithms, it is also starting a random solution, through an iterative search for the optimal solution, which is also used to uate the quality through fitness solution, but it is simpler than genetic algorithm rules It has no genetic algorithm " crossover" (Crossover) and " variant" (Mutation) operation, which by following the current search to find the optimal value to the global optimum. This algorithm is its easy implementation, high accuracy, fast convergence, etc. attracted academic attention and show its superiority in solving practical problems. PSO algorithm is a parallel algorithm. BP (Back Propagation) neural network is a 1986 team of scientists headed by Rumelhart and McC
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Author: 艾岳巍 |
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Description: 基于MAPREDUCE的PSO设计论文 有详细数学算法和流程-The PSO-based design thesis MAPREDUCE
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Author: syalr |
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Description: 基本粒子群优化算法（PSO），全局性，并行性，高效的群体智能算法。-Particle swarm optimization (PSO), the global, parallel and efficient swarm intelligence algorithm.
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Description: 粒子群算法matlab代码吐血推荐。粒子群算法，也称粒子群优化算法，是近年来发展起来的一种新的进化算法。它是从随机解出发，通过迭代寻找最优解，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。-PSO algorithm matlab code recommended blood. Particle swarm optimization, also known as PSO, is a new evolutionary algorithm developed in recent years. It is starting random solutions, through iterative find the optimal solution, by adapting to uate the quality of the solution, but it' s much simpler than genetic algorithm rule, it is not genetic algorithm " crossover" (Crossover) and " variation" (Mutation ) operation, which by following the current optimum value to search to find the global optimum. This algorithm is its easy implementation, high accuracy, and fast convergence advantages attracted academic attention, and demonstrated its superiority in solving practical problems. Particle swarm algorithm is a parallel algorithm.
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Author: Charlie |
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Description: Particle swarm optimization algorithm, also known as particle swarm optimization algorithm (Swarm Optimization Particle), abbreviated as PSO, is a parallel algorithm on the basis of the observation of the behavior of animal clusters, the use of groups of individuals in the information sharing to make the whole group of movement in the problem solving space generated the disorder to the orderly evolution process, thereby obtaining the optimal solution. Particle swarm optimization algorithm and simulated annealing algorithm, it is also the random solution, through the iterative search for the optimal solution, it is through the fitness to uate the quality of the solution, but it is more simple than the genetic algorithm rules, it does not have the genetic algorithm crossover (Crossover) and Mutation operation, it follows the current search to the optimal value to find the global optimal.-Particle swarm optimization algorithm, also known as particle swarm optimization algorithm (Swarm Optimization Particle), abbreviated as PSO, is a parallel algorithm on the basis of the observation of the behavior of animal clusters, the use of groups of individuals in the information sharing to make the whole group of movement in the problem solving space generated the disorder to the orderly evolution process, thereby obtaining the optimal solution. Particle swarm optimization algorithm and simulated annealing algorithm, it is also the random solution, through the iterative search for the optimal solution, it is through the fitness to uate the quality of the solution, but it is more simple than the genetic algorithm rules, it does not have the genetic algorithm crossover (Crossover) and Mutation operation, it follows the current search to the optimal value to find the global optimal.
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Author: jianglantian |
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Description: 粒子群算法（PSO）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。该程序适用于MATLAB中粒子群算法的实现。-
Similar to the one of simulated annealing algorithm and particle swarm optimization (PSO) belongs to the evolutionary algorithm, it is also a departure random solutions, through iterative find the optimal solution, it is also uated by the fitness of the quality of the solution, but it is more than Genetic Algorithm Rules more simple, it does not have the genetic algorithm cross (crossover) and variation (mutation) operation, follow it through to the current search to find the optimal value of the global optimum. This algorithm is its easy implementation, high precision, rapid convergence, etc. attracted academic attention, and demonstrated its superiority in solving practical problems. Particle swarm algorithm is a parallel algorithm. This procedure applies to implement MATLAB Particle Swarm Optimization.
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Author: 原文宾 |
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Description: 粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO， 是近年来由J. Kennedy和R. C. Eberhart等[1] 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。-Swarm optimization, also known as PSO (Particle Swarm Optimization), abbreviated as PSO, in recent years, one J. Kennedy and RC Eberhart et al. [1] developed a new evolutionary algorithm (Evolutionary Algorithm- EA). One of PSO algorithm and simulated annealing algorithm is similar to evolutionary algorithms, it is also a departure random solutions, through iterative find the optimal solution, it is also uated by the fitness of the solution quality, but it' s simpler than genetic algorithm rules it is no genetic algorithm " cross" (crossover) and " variation" (mutation) operation, follow it through to the current search to find the optimal value of the global optimum. This algorithm is its easy implementation, high precision, rapid convergence, etc. attracted academic attention, and demonstrated its superiority in solving practical problems. Particle swarm algorithm is a parallel algorithm.
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Author: snowtiger |
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Description: 粒子群算法是一种并行算法。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。(Particle swarm optimization (PSO) is a parallel algorithm. It seeks the global optimal by following the optimal value of the current search. This algorithm has attracted the attention of academic circles for its advantages of easy realization, high precision and fast convergence, and shows its superiority in solving practical problems.)
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Author: zkhbqhd |
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Description: 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。(The PSO algorithm is a kind of evolutionary algorithm. Similar to the simulated annealing algorithm, it also starts from the random solution and iteratively finds the optimal solution. It also evaluates the quality of the solution through fitness, but it is simpler than the genetic algorithm rules. It does not have the "Crossover" and "Mutation" operations of the genetic algorithm. It seeks the global optimum by following the current searched optimal value. This kind of algorithm has attracted much attention from the academic community because of its advantages of easy implementation, high precision and fast convergence. It also shows its superiority in solving practical problems. Particle swarm algorithm is a parallel algorithm.)
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Author: cinderella345 |
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Description: 粒子群算法，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。(The particle swarm optimization (PSO) algorithm, which is one of the evolutionary algorithms, is similar to the simulated annealing algorithm. It also starts from the random solution to find the optimal solution by iteration. It also evaluates the quality of the solution by the fitness, but it is more simple than the genetic algorithm rule, and it has no genetic algorithm "Crossover" and "variation". "(Mutation) operation, it seeks the global optimum by following the best value that is currently searched. This algorithm has attracted the attention of the academic community for its advantages of easy realization, high accuracy and fast convergence, and has shown its superiority in solving practical problems. Particle swarm optimization (PSO) is a parallel algorithm.)
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Author: 绝情逆空 |
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