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Advancement of Computing Methodology, Data Analysis, Enterprise Operation Management and Decision Support System Based on the Eleventh ICMSEM Proceedings Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Fast Multiobjective Hybrid Evolutionary Algorithm Based on Mixed Sampling Strategy

Authors : Wenqiang Zhang, Yu Wang, Chunxiao Wang, Le Xiao, Mitsuo Gen

Published in: Proceedings of the Eleventh International Conference on Management Science and Engineering Management

Publisher: Springer International Publishing

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Abstract

In this paper, a fast multiobjective hybrid evolutionary algorithm (MOHEA) is proposed to solve the multiobjective optimization problem (MOOP) in achieving a balance between convergence and distribution with computational complexity. The proposed algorithm, MOHEA, improves the vector evaluated genetic algorithm (VEGA) by combing a new sampling strategy according to the Pareto dominating and dominated relationship-based fitness function. VEGA is good at searching the edge region of the Pareto front, but it has neglected the central area of the Pareto front, and the new sampling strategy prefers the center region of the Pareto front. The mixed sampling strategy improves the convergence performance and the distribution performance while reducing the computational time. Simulation experiments on multiobjective test problems show that, compared with NSGA-II and SPEA2, the fast multiobjective hybrid evolutionary algorithm is better in the two aspects of convergence and distribution, and has obvious advantages in the efficiency.

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Metadata
Title
Fast Multiobjective Hybrid Evolutionary Algorithm Based on Mixed Sampling Strategy
Authors
Wenqiang Zhang
Yu Wang
Chunxiao Wang
Le Xiao
Mitsuo Gen
Copyright Year
2018
Book
Proceedings of the Eleventh International Conference on Management Science and Engineering Management

Print ISBN: 978-3-319-59279-4

Electronic ISBN: 978-3-319-59280-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-59280-0_8

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