搜索资源列表
PS0-TSP
- 多目标问题，粒子群优化算法，解决单旅行商问题-Multi-objective problem, particle swarm optimization algorithm to solve the traveling salesman problem alone
MOPSO
- 多目标粒子群优化，是一种基于种群的进化算法，每次迭代能产生出一组非劣解，经过适当的扩展可适于求解多目标优化问题。-Multi-objective particle swarm optimization, evolutionary algorithm is a population-based, each iteration produces a set of non-dominated solutions, through appropriate extension may be adapted
mymopso
- 用多目标粒子群优化来求解多目标优化问题，该方法可求解各种多目标问题。程序内包含多种测试函数，和适应度函数求解程序及目标函数和约束函数的具体程序说明。-Multi-objective particle swarm optimization to solve the multi-objective optimization problem which can be solved a variety of multi-objective problem. Specific procedures wit
Biogeography-Optimization-algorithm
- 生物地理学优化算法，作者Dan Simon，是在遗传算法和粒子群算法的基础上发展来的，适用于解决高维度的，多目标的最优化问题。-Biogeography optimization algorithm, author Dan Simon, is based on genetic algorithm and particle swarm algorithm development to apply to solve high-dimensional, multi-objective optimiza
pos
- 基于MATLAB的带压缩系数的多目标粒子群优化算法，不同于基本多目标粒子群算法带有权重系数，其鲁棒性和收敛性更好-Multi-objective optimization algorithm based on particle swarm MATLAB with the compression factor, unlike the basic multi-objective Particle Swarm Optimization with the weight coefficients, its
IM-MOPSO
- 一种改善的多目标粒子群算法，包括了多个实例的实验！-A improved multi-objective particle swarm optimization, including several instaces.
MOPSO
- 用Matlab实现多目标粒子群算法（MOPSO），该算法是以结构化的方式实现，或许对你的研究项目有所帮助。-Using Matlab to achieve a multi objective particle swarm optimization algorithm (MOPSO), the algorithm is structured in a way to achieve, perhaps to help your research projects.
NSPSO
- 非支配排序的多目标微粒群算法NSPSO，在标准微粒群算法中加入NSGA2的思想-Multi objective particle swarm optimization algorithm based on non dominated sorting NSPSO, in standard particle swarm optimization, the non dominated sorting of NSGA2.
MPSO
- 适合初学者学习matlab编程的多目标粒子群算法，可以适当更改成为单目标粒子群算法-Matlab programming for beginners to learn the multi objective particle swarm optimization algorithm can be modified to become a single objective particle swarm algorithm
MOPSO-MATLAB
- 多目标粒子群算法是模拟动物群体的社会行为，找到一个最优设计点的过程比作这些生物的觅食活动。换句话说，这些例子在设计空间中寻找最好的位置。-Multi-objective Particle Swarm social behavior is simulated animal groups, a process to find the optimal design point likened foraging activity of these organisms. In other words, t
PSO-MATLAB
- 一个例子的最佳位置和沿着当前速度和惯性方向的邻元素被用来决定下一个例子的位子。多目标粒子群的优化算法简介如下-An example of the best location and along the current speed and direction of inertia neighbors are used to determine the next example of the seat. Introduction to multi-objective optimization alg
mopso
- 可靠性算法,利用多目标粒子群算法对多目标值进行运算-Reliability algorithm, multi-objective particle swarm optimization algorithm for multi-target value operation
MOPSO
- Multi-Object Particle Swarm Optimization (MOPSO)
MOPSO-based-on-adaptive-mutaiton
- 基于自适应变异的对多目标粒子群算法的改进算法-Based on the multi-objective particle swarm algorithm for improved algorithm of adaptive mutation
MoPSO
- 多目标粒子群算法,解决多目标问题，实用性比较广，测试可以运行-Multi-objective particle swarm optimization, multi-objective problem solving, practical and broad, tests can be run
pos
- 改进后的粒子群算法，可用于求解规划问题，路径问题和相关多目标组合优化问题-The improved particle swarm optimization (pso) algorithm, can be used to solve the planning problem, the routing problem and the relevant multi-objective combinatorial optimization problem
matlabPSO
- 提出了一种新的多目标粒子群优化(MOPSO)算法，该算法采用自适应网格方法来估计非劣解集中粒子的密度信息、平衡全局和局 部搜索能力的 Pareto 最优解的搜索机制、删除品质差的多余粒子的 Archive 集的修剪技术。通过对三峡梯级多目标优化调度问题的计算， 表明该算法是求解大规模复杂多目标优化问题的一种有效手段。-A new multi-objective particle swarm optimization(MOPSO) is proposed. The proposed alg
MOPSO
- multi objective Particle swarm optimization
1-s2.0-S0925231212007722-main
- 不确定环境下的机器人路径规划的多目标粒子群优化算法-Robot path planning in uncertain environment using multi-objective particle swarm optimization
PSO
- 基于粒子群算法的多目标搜索算法，本案例采用多目标粒子群算法求解多目标背包问题。-Multiple target search algorithm based on particle swarm optimization (pso) algorithm, this case USES the multi-objective particle swarm optimization (pso) algorithm to solve multi-objective knapsack problem.