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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
求解机械优化的Pareto多目标中心粒子群算法
- 摘要：针对基于权重法的多目标算法无法求解约束多目标问题的缺陷，将中心粒子群算法与Pareto 解集搜索算法相结合，提出 一种Pareto 多目标中心粒子群算法。将此方法用来优化气门弹簧的模型，实验结果表明，该优化方法能够快速准确地收敛于Pareto 解集，并且使其对应的目标域均匀地分布于Pareto最优目标域。
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- 基于生物免疫系统的自适应学习、免疫记忆、抗体多样性及动态平衡维持等功能,提出一种动态多目标免疫 优化算法处理动态多目标优化问题.算法设计中,依据自适应ζ邻域及抗体所处位置设计抗体的亲和力,基于Pa- reto控制的概念,利用分层选择确定参与进化的抗体,经由克隆扩张及自适应高斯变异,提高群体的平均亲和力,利 用免疫记忆、动态维持和Average linkage聚类方法,设计环境识别规则和记忆池,借助3种不同类型的动态多目标 测试问题,通过与出众的动态环境优化算法比较,数值实验表明所
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- 摘 要:提出一种新的基于Pareto多目标进化免疫算法(PMEIA)。算法在每一代进化群体中选取最优非支配抗体保存到记忆细胞文档中 同时引入Parzen窗估计法计算记忆细胞的熵值,根据熵值对记忆细胞文档进行动更新,使算法向着理想Pareto最优边界搜索。此外,算法基于点在目标空间分况进行克隆选择,有利于得到分布较广的Pareto最优边界,且加快了收敛速度。与已有算法相比, PMEIA在收敛性、多样性,以及解的分布性方面都得到很好的提高。-Abstract:This paperproposed a
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- 摘要:提出了一种新型的人工免疫算法用来解决多目标函数优化问题。基于自然免疫系统固有的优良特性对算法进行了设计和分析。 最后,算法对3个较复杂的多目标问题进行了优化,优化结果能很好地覆盖问题的Pareto最优面,并且把算法与某些混合遗传算法进行 了对比实验,表明人工免疫算法在解决多目标优化问题上具有可观的研究前景。 -Abstract:In order to effectively solve multiobjective optimization problems, a novel
nsga2
- 基于NSGA-Ⅱ算法的区域覆盖卫星星座优化将一种基于Pareto最优概念的快速非支配排序遗传算法(NSGA-Ⅱ)引入区域覆盖侦察卫星星座的多目标优化设计-Algorithm based on NSGA-Ⅱ regional coverage satellite constellation will be based on the concept of Pareto optimal fast non-dominated sorting genetic algorithm (NSGA-Ⅱ) the
SPEA2-Improving-the-Strength-Pareto-Evolutionary-
- SPEA2 Improving the Strength Pareto Evolutionary Algorithm
ICUFN-2012
- The performance of a visible light communication system is greatly affected by LED layout setting. Received power and SNR can be increased or decreased by changing LED placement. However, there is trade-off between received power and SNR. This makes
ICUFN-Receiver-Power-and-SNR-optimization-for-Vis
- The performance of a visible light communication system is greatly affected by LED layout setting. Received power and SNR can be increased or decreased by changing LED placement. However, there is trade-off between received power and SNR. This makes
A-Pareto-Strength-SCE-UA-Algorithm
- 火箭炮交流位置伺服系统的鲁棒最优控制 ，需要的可以下了-A Pareto Strength SCE-UA Algorithm for Reservoir OPtimization OPeration
1-s2.0-S0032591011005584-main
- Modeling and Pareto optimization of gas cyclone separator performance using RBF type artificial neural networks and genetic algorithms
1-s2.0-S0142061513003657-main
- Optimization of India’s electricity generation portfolio using intelligent Pareto-search genetic algorithm
Emergency-Scheduling-Pareto-solution
- 应急调度 粒子群算法 pareto解集 非梯度爬山搜索-Emergency Scheduling particle swarm algorithm Pareto solution set of non gradient hill-climbing search
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
multi-obj-opt
- 国外大牛在信息科学上发表的关于多目标优化方法的文章，非常值得学习。-we explore the implications of using decomposition-based methods over Pareto-based methods on algorithm convergence a probabilistic point of view. Namely, we investigate whether there is an advantage of using a de
multi-objective power flow optimization
- 构建了含VSC-HVDC的交直流系统多目标最优潮流模型；针对此模型连续变量和离散变量共存的特点，提出了内点法和NSGA2算法相结合的交替求解算法，可获得多个Pareto最优解，并具有较高的计算效率(Considering the coexistence of continuous and discrete variables in this model,an alternative solution method based on the interior point method and NS
An Indicator Based Multi-Objective Evolutionary Algorithm with Reference Point Adaptation for Better Versatility
- During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shap