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2013 | OriginalPaper | Buchkapitel

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

verfasst von : Li-li Zhang, Feng Xu, Juan Hu

Erschienen in: Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013

Verlag: Springer Berlin Heidelberg

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Abstract

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.

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Metadaten
Titel
Multi-Objective Genetic Algorithm with Complex Constraints Based on Colony Classify
verfasst von
Li-li Zhang
Feng Xu
Juan Hu
Copyright-Jahr
2013
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-37502-6_20