搜索资源列表
Multi-Agent-Particle-Swarm-Algorithm
- 结合多智能体的学习、协调策略及粒子群算法，提出了一种基于多智能体粒子群优化的配电网络重构方法。该方法采用粒子群算法的拓扑结构来构建多智能体的体系结构，在多智能体系统中，每一个粒子作为一个智能体，通过与邻域的智能体竞争、合作。能够更快、更精确地收敛到全局最优解。粒子的更新规则减少了算法不可行解的产生，提高了算法效率。实验结果表明，该方法具有很高的搜索效率和寻优性能。-Combining the study of multi-agent technology，coordinating strateg
multi-objective-
- 改进的多目标粒子群算法，包括多个测试函数 对程序中的部分参数进行修改将更好地求解某些函数-Improved multi-objective particle swarm algorithm, modify some parameters of the program including a plurality of test functions will be better for solving certain functions
Multi-objective-particle-swarm
- 多目标粒子群优化算法 多目标粒子群优化算法 多目标粒子群优化-Multi-objective particle swarm optimizationMulti-objective particle swarm optimizationMulti-objective particle swarm optimization
pso-constraints-multi
- 多目标粒子群算法，最优的解决方案，使用一个外部文件管理，拥塞机制筛选，灰色设计验过滤。-Multi-objective particle swarm algorithm, the optimal solution using an external file management, congestion mechanism screening, gray design posteriori filtering.
DMS-PSO_code
- 动态多群粒子群代码-基于J.J.Liang2005年得文章-dynamic multi-swarm optimizer
The-Multi-user-Detection-ALGORITHM
- 联合智能(JI-MUD)多用户检测算法是由粒子群优化(PSO)算法,遗传算法(GA)和模拟退火(SA)算法-the joint intelligent multi-user detection (JI-MUD) algorithm which was composed by particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and simulated annealing (SA) algorithm was p
The-Multi-user-Detection-paper
- 提出了基于联合智能算法的MIMO-OFDM系统多用户检测算法。联合智能算法结合了粒子群优化算法、遗传算法、模拟退火算法的思想。-Joint intelligent algorithm is proposed based on multi-user detection algorithm of MIMO- OFDM system. Combined intelligent algorithm combining particle swarm optimization algorithm and g
Particle-Swarm
- 基于Pareto支配的多目标粒子群算法程序，用matlab设计实现，已经通过多个公认测试函数测试，结果良好。 -Based on multi-objective particle swarm algorithm Pareto domination, using matlab design implementation, test function has been recognized by a number of tests with good results.
MGAA
- 本算法是基于遗传算法的多种群算法，能够对全局的多个目标进行同时寻优，在全局中能够取得较好的全局最优值。-This is a multi-swarm algorithm based on genetic algorithm, capable of multiple targets simultaneously global optimization, in the big picture can achieve better global optimum.
Particle-swarm-optimization
- 粒子群算法，实现多维参数寻找最优解，含有代码说明-Particle swarm optimization, multi-dimensional parameters to find the optimal solution containing code Descr iption
Multi-objective-particle-swarm
- 利用matlab编写的多目标粒子群算法可以很快得到最优值。-Use matlab write multi-objective particle swarm algorithm can quickly obtain optimal value.
Codes-of-BSA-and-CSO
- Chicken Swarm Optimization（CSO）鸡群优化算法，2014年提出的群智能优化算法。 Bird Swarm Algorithm（BSA）鸟群算法，2015年最新的群智能优化算法。 作为两种全新的群智能优化算法，CSO和BSA都具有简单，良好扩展性的特点，是天然的多种群算法！ http://cn.mathworks.com/matlabcentral/profile/authors/3317597-xian-bing-meng 有关算法信息，可在上述网站查询
particle-swarm-algorithm
- 该资料为多目标粒子群求优MATLAB代码。改进的多目标粒子群算法，包括多个测试函数 对程序中的部分参数进行修改将更好地求解某些函数-The data is a multi object particle swarm optimization MATLAB code. The improved multi-objective particle swarm optimization algorithm, including the number of test functions in the pr
Swarm-robot
- 群机器人在动态未知环境下，多目标搜索；通过自组织任务分工实现搜索并行进行，且能够有效的避开动态及凸障碍物。-Group of robots in dynamic unknown environments, multi-objective search Through the self-organization tasks to achieve parallel search, and can effectively avoid dynamic and convex obstacles.
multi-objective-PSO
- 多目标优化的粒子群算法，基于粒子群改进算法，设置非劣解，对多目标进行优化。-Multi-objective optimization of particle swarm optimization (pso) algorithm
m-particle-swarm-optimization
- 多目标粒子群算法，在基本粒子群算法的基础上进行了改进，程序简单明了。-Multi-objective particle swarm algorithm based on particle swarm algorithm has been improved, the program is simple and straightforward.
Three-Phase-Multi-Level-SVPWM
- Partical swarm optimization technique for analysis of load flow
Swarm-Optimization-Algorithm
- Swarm Optimization Algorithm：包含各类PSO算法，可以用于多目标函数的优化，避免优化结果出现局部最优，可用于各个算法收敛性间的对比试验。- Swarm Optimization Algorithm: it contains all kinds of PSO algorithm can be used to optimize multi-objective function optimization results to avoid local optimum co
Particle-Swarm-Optimization
- 本文提出变量随机分解策略，增加关联变量分配到同组的概率，使得算法更好的保留变量间的关联性，并将合作协同进化框架融合到算法中，提出了基于大规模变量分解的多目标粒子群优化算法-In this paper, a stochastic variable decomposition strategy is proposed to increase the probability of assigning related variables to the same group, which makes th
HMPSO
- A hybrid multi-swarm particle swarm optimization to solve constrained optimization