Multi-Objective Genetic Algorithm with Complex Constraints Based on Colony Classify

被引：0

作者：

机构：

[1] Mathematics Department, School of Science, Anhui University of Science and Technology, Huainan

[2] The Foundation Department of Huainan Vocational Technical College, Huainan

来源：

Zhang, L.-L. (xiamilao@126.com) | 1600年 / Springer Verlag卷 / 212期

关键词：

Clustering analysis; Constraint condition; Genetic algorithm; Multi-objective optimization;

D O I：

10.1007/978-3-642-37502-6_20

中图分类号：

学科分类号：

摘要：

The paper presents a constraint-handling approach for multi-objective optimization. The general idea is shown as follow: Firstly, the population was classified into two groups: feasible population and infeasible population. Secondly, feasible population was classified into Pareto population and un-Pareto population. Thirdly, the Pareto population was defied with k-average classify approach into colony Pareto population and in-colony Pareto population. Last, R-fitness was given to each population. Simulation results show that the algorithm not only improves the rate of convergence but also can find feasible Pareto solutions distribute abroad and even. © Springer-Verlag Berlin Heidelberg 2013.

引用

页码：163 / 170

页数：7

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