搜索资源列表
MultiobjectiveGeneticAlgorithm
- 多目标遗传算法/用法不用多说、要用的赶快下载吧-Multi-objective Genetic Algorithm/usage Needless to say, want to use to download it as soon as possible
ParetoOptimalTopologies
- MATLAB file pareto optimal
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.
MG1Pareto
- M G 1 排队模型，matlab代码，含说明文档。 G采用的是Pareto分布。值得学习-M g q queuing model , matlab code
NSGA-II
- matlab环境下实现的非支配排序遗传算法（NSGA-II），该算法在快速找到Pareto前沿和保持种群多样性方面都有很好的效果-matlab environment to achieve non-dominated Sorting Genetic Algorithm (NSGA-II), the algorithm quickly find the Pareto frontier and maintaining the diversity of the population has a goo
ypea121-mopso
- Home Metaheuristics Machine Learning Multiobjective Optimization Fuzzy Systems Applications Home \ Multiobjective Optimization \ Multi-Objective PSO in MATLAB ypea121-mopso Multi-Objective PSO in MATLAB in Multiob
Multiobjectivesearchingalgorithm
- 压缩包内MATLAB程序演示多目标perota优化问题，从种群初始化，种群更新，更新个体最优粒子，非劣解筛选进行编程，最后给出了仿真结果，由图可知，算法搜索到的非劣解构成了Pareto面，算法搜索取得了很好的效果，谢谢 希望对大家有帮助。-Compressed package MATLAB program demonstrates perota multi-objective optimization problem, populations initialization, populatio
polarPcolor
- matlab改进的序列二次规范法求解多目标函数的最优值问题，能够求出pareto解集-Matlab improved sequence of two times the standard method to solve the multi-objective function of the optimal value problem, can find the Pareto solution set
NSGA-II
- 非支配排序的遗传算法matlab实现，pareto原理求解多目标问题(Matlab implementation of nsga2 with non dominated sorting and Pareto principle to solve multi-objective problems)
nsga
- 实例分析，运用MATLAB中自带的多目标遗传算法对多目标函数进行计算，找到帕累托最优解。(Case study shows that the multi-objective genetic algorithm in MATLAB is used to calculate the multi-objective function and find the Pareto optimal solution.)
NSGA
- 多目标遗传算法是NSGA-II[1]（改进的非支配排序算法）,该遗传算法相比于其它的多目标遗传算法有如下优点：传统的非支配排序算法的复杂度为 ，而NSGA-II的复杂度为 ，其中M为目标函数的个数，N为种群中的个体数。引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度。采用拥挤度和拥挤度比较算子，不但克服了NSGA中需要人为指定共享参数的缺陷，而且将其作为种群中个体间的比较标准，使得准Pareto域中的个体能均匀地扩展到整个Pareto域，保证了种群的多样性