搜索资源列表
MOEA-NSGA-II
- 基于进化算法的多目标优化,以matlab程式所寫成,請大家參考參考-evolutionary algorithm based on the multi-objective optimization to written Matlab program, please refer to reference
MOEA-NSGA-II
- 多目标优化进化算法目前公认效果收敛性最好的算法NSGA2源码，内有多目标算法的工具箱，对实现其他多目标优化算法很有帮助-Multi-objective optimization evolutionary algorithm is currently recognized as the effect of convergence of the algorithm NSGA2 best source, with a number of ta
MOEA
- 本例程解决了一个两个目标的优化问题，采用的是权重系数法求解。求解过程中采用了代沟。程序中有详细的注释-This routine solves a two-goal optimization problem, using the weight coefficient method. Solving process used in the generation gap. Procedures are detailed in the Note
MOEA-NSGA-II
- matlab实现最优化选择的一个算法，大家可以下下来参考，我也是从别处down的，谢谢原作者-matlab optimize the choice of an algorithm, we can refer to the next down, I am also down elsewhere, thank you, original author
MOEA-NSGA-II
- 多目标遗传算法通用编程包，是解决复杂多目标问题的通用程序-MOEA-NSGA
MOEA-NSGA-II
- genetic algorithm, simulated annealing, singleobjective, particle swarm optimi..., optimization, classes
MOEA-D-DE
- Multi objective evoutionary algorithms
de
- Epsilon-MOEA in C and C++
MOEA-D-Continuous
- 多目标优化程序，国际顶级期刊IEEE EC 经典算法-multi-objective optimization algorithm presented in journal of IEEE trans
10[1].1.1.42.2856
- 一篇knowles的大作，对于学习多目标进化算法有着深层次的作用。-MOEA
SPEA2
- 强度PARETO算法，非常经典，也是一个学习多目标进化算法的经典作品。-MOEA
moea
- NSGA2 和 QMEA 的java实现-NSGA2 and QMEA in JAVA
MOEA-D-DE
- 多目标差分进化算法，实现了多个目标共同进化的启发式进行算法-MOEA-D-DE
MOEA
- 一篇带约束条件的基于多播QOS多目标算法的论文。-Constraints QoS Multicast Routing Based on MOEA
MOEA-D-ContinuousCp2Bp2B
- MOEA/D 优化算法源程序。这是新兴的公认很好的一种多目标优化算法-for the MOEA/D algorithm
CEC09-MOEA-Codes-of-Problems
- CEC09年群体智能会议的测试问题的代码-CEC09-MOEA-Codes-of-Problems matlab code
MOEA
- 采用MOEA策略解决多重约束的方程求最优解问题。-to solve an multi-constraint equation problem using MOEA algorithm
MOEA-D
- 实现对多目标问题的求解，能够逼近真实的parato前沿面，分布性特别好(MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition)
YPEA124 MOEA-D
- MOEA-D详细代码 数值算法/人工智能/matlab例程(MOEA-D detailed code, numerical algorithm / AI /matlab routines.)
MOEA
- 多目标进化算法，内容很全，所有函数M文件都有。（不含文本说明）(MOEA Multi-objective evolutionary algorithm, the content is very complete, all function M files have. (excluding text descr iptions))