Description: NSGA原代码，很多人在找这个，对搞智能优化很有帮助-NSGA original code, a lot of people looking for this, very helpful to engage in intelligent optimization Platform: |
Size: 151552 |
Author:王军 |
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Description: 实现了多目标遗传算法NSGA2，并带有详细注释及相关论文，读者只需根据具体问题简要修改，即可使用。-Achieved a multi-objective genetic algorithm NSGA2, with detailed notes and related papers, the reader just a brief specific issues in accordance with changes to use. Platform: |
Size: 126976 |
Author:赵爽 |
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Description: 基于MATLAB平台的Deb多目标遗传算法之NSGA-II，包含帮助文件-Deb based on MATLAB platform of multi-objective genetic algorithm NSGA-II, contains the Help files Platform: |
Size: 172032 |
Author:zangtianlei |
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Description: 最权威版nsga2的matlab版 ，国际EMOO研发小组的成果。-The most authoritative version of nsga2 the matlab version of the results of the international EMOO development team. Platform: |
Size: 7168 |
Author:wsf211 |
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Description: 改进的NSGA-II的简单例子，包含NSGA-II的基本思想，直接在matlab中就可以运行。-Improve the NSGA- II simple examples, including the NSGA- II, the basic idea of directly can run in matlab. Platform: |
Size: 1024 |
Author:王振 |
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Description: Downloads The download link of this project follows. Portfolio Optimization using Classic Methods and Intelligent Methods (PSO, ICA, NSGA-II, and SPEA2) in MATLAB Download Platform: |
Size: 295936 |
Author:vinc |
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Description: 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。（消除了共享参数）。(Multi-objective genetic algorithm is nsga-ii [1] (improved non-dominant sorting algorithm), which has the following advantages compared with other multi-objective genetic algorithms: the complexity of the traditional non-dominant sorting algorithm is, while the complexity of nsga-ii is, where M is the number of objective functions and N is the number of individuals in the population.The introduction of elite strategy to ensure that some good individuals in the evolutionary process will not be discarded, thus improving the accuracy of the optimization results.The comparison operator of crowding degree and crowding degree not only overcomes the defect that NSGA needs to specify the Shared parameter artificially, but also takes it as the comparison standard between individuals in the population, so that individuals in the quasi-pareto domain can uniformly expand to the whole Pareto domain, ensuring the diversity of the population.(eliminating Shared parameters).) Platform: |
Size: 16384 |
Author:浅浪 |
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