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International Conference on Natural Computation

ICNC 2005: Advances in Natural Computation pp 392–398Cite as

Improving Multiobjective Evolutionary Algorithm by Adaptive Fitness and Space Division

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3612))

Abstract

In this paper, a novel evolutionary algorithm based on adaptive multiple fitness functions and adaptive objective space division for multiobjective optimization is proposed. It can overcome the shortcoming of those using the weighted sum of objectives as the fitness functions, and find uniformly distributed solutions over the entire Pareto front for non-convex and complex multiobjective programming. First, we divide the objective space into multiple regions with about the same size by uniform design adaptively, then adaptively define multiple fitness functions to search these regions, respectively. As a result, the Pareto solutions found on each region are adaptively changed and eventually are uniformly distributed over the entire Pareto front. We execute the proposed algorithm to solve five standard test functions and compare performance with that of four widely used algorithms. The results show that the proposed algorithm can generate widely spread and uniformly distributed solutions over the entire Pareto front, and perform better than the compared algorithms.

This work was supported by the National Natural Science Foundation of China (60374063) and SRG: 7001639 of City University of Hong Kong.

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Author information

Authors and Affiliations

  1. School of Computer, Xidian University, Xi’an, 710071, China

    Yuping Wang

  2. Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon Tong, Hong Kong

    Chuangyin Dang

Authors
  1. Yuping Wang
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  2. Chuangyin Dang
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Wang, Y., Dang, C. (2005). Improving Multiobjective Evolutionary Algorithm by Adaptive Fitness and Space Division. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_47

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  • DOI: https://doi.org/10.1007/11539902_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28320-1

  • Online ISBN: 978-3-540-31863-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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