搜索资源列表
MOEA
- 多目标进化算法，思想主要是印度学者的NSGA-2 用于处理多目标实值优化问题-muliti-objective evolution
multi
- 带等式约束的多目标遗传算法工具箱nondominated sorting genetic algorithmⅡ，NSGA-Ⅱ优化源代码-With equality constraint multi-objective genetic algorithm toolbox nondominated sorting genetic algorithm Ⅱ NSGA-II optimized source code
nsga2-v1.1
- nsga算法在linux上的C程序，多目标优化程序。官方原版(NSGA algorithm on the Linux C program, multi objective optimization program. Official original)
GRMNCN
- nsga2_c多目标优化算法：NSGA-II算法()
duxpd
- nsga2_c多目标优化算法：NSGA-II算法()
evaluate_objective
- 利用NSGA-II算法实现水资源配置多目标优化问题——目标函数；(Realization of multi-objective optimization of water resources allocation based on NSGA-II algorithm_objectives;)
genetic_operator
- 利用NSGA-II算法实现水资源配置多目标优化问题——基因操作；(Realization of multi-objective optimization of water resources allocation based on NSGA-II algorithm_genetic operator)
initialize_variables
- 利用NSGA-II算法实现水资源配置多目标优化问题——初始变量；(Realization of multi-objective optimization of water resources allocation based on NSGA-II algorithm_initialize variables)
nsga_2
- 利用NSGA-II算法实现水资源配置多目标优化问题——nsga2算法(Realization of multi-objective optimization of water resources allocation based on NSGA-II algorithm_nsga2)
Multi-objective-evolutionary
- NSGA的源程序，是多目标进化算法的智能算法，可用于多目标优化与决策等方面的计算(NSGA source, multi-objective evolutionary algorithm intelligent algorithm can be used to calculate other multi-objective optimization and decision-making)
带约束的遗传优化算法
- 带约束的多目标遗传优化算法NSGA-II(Constrained Multi-objective Genetic Algorithms NSGA-II)
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
NSGA-II多目标优化算法matlab程序
- 遗传算法程序NSGA2，关于移动机器人路径规划。(Matlab program of path planning based on NSGA2 genetic algorithm)