2017 A Maximal Clique Based Multiobjective Evolutionary algorithm for overlapping community detection 论文PPT讲解

matlab快速非支配算法代码标准，约束和并行多目标EGO算法 1.无约束多目标EGO算法 使用EIM（预期改进矩阵）标准的无约束多目标EGO算法，与最新的多目标EI标准相比，评估起来便宜得多。 有关EIM标准的详细说明，请参阅[1]。 2.约束多目标EGO算法 使用CEIM（约束预期改进矩阵）准则的约束多目标EGO算法，可以解决昂贵的约束多目标问题。 3.并行多目标EGO算法 使用PEIM（伪期望改进矩阵）标准的并行多目标EGO算法，该算法能够在每个周期中选择多个候选对象以进行并行评估[2]。 4.并行约束多目标EGO算法 使用PCEIM（伪约束预期改进矩阵）标准的并行约束多目标EGO算法，该算法能够在每个周期中选择多个候选对象以进行并行评估[2]。 5.注意事项 dace工具箱[3]用于在实现中构建Kriging模型。 Yi Cao [4]的非支配排序方法用于从所有设计点识别非支配前沿。 超量指标是使用[5] Nicola Beume等人的更快算法来计算的。 （2009）。 EIM和PEIM标准都可以通过DE [6]算法最大化。 参考 D.Zhan，Y.Cheng，Liu J.Li

K. Miettinen, Nonlinear Multiobjective Optimization. Norwell, A:Kluwer, 1999. Problems with multiple objectives and criteria are generally known as multiple criteria optimization or multiple criteria decision-making (MCDM) problems. So far, these types of problems have typically been modelled and solved by means of linear programming. However, many real-life phenomena are of a nonlinear nature, which is why we need tools for nonlinear programming capable of handling several conflicting or incommensurable objectives. In this case, methods of traditional single objective optimization and linear programming are not enough; we need new ways of thinking, new concepts, and new methods - nonlinear multiobjective optimization. Nonlinear Multiobjective Optimization provides an extensive, up-to-date, self-contained and consistent survey, review of the literature and of the state of the art on nonlinear (deterministic) multiobjective optimization, its methods, its theory and its background. The amount of literature on multiobjective optimization is immense. The treatment in this book is based on approximately 1500 publications in English printed mainly after the year 1980. Problems related to real-life applications often contain irregularities and nonsmoothnesses. The treatment of nondifferentiable multiobjective optimization in the literature is rather rare. For this reason, this book contains material about the possibilities, background, theory and methods of nondifferentiable multiobjective optimization as well. This book is intended for both researchers and students in the areas of (applied) mathematics, engineering, economics, operations research and management science; it is meant for both professionals and practitioners in many different fields of application. The intention has been to provide a consistent summary that may help in selecting an appropriate method for the problem to be solved. It is hoped the extensive bibliography will be of value to researchers.

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MOMMOP: Multiobjective optimization for locating multiple optimal solutions of multimodal optimization problems

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Theory of Multiobjective Optimization，多目标优化理论