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A multiobjective cellular genetic algorithm based on 3D structure and cosine crowding measurement

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Abstract

Multiobjective cellular genetic algorithms (MOcGAs) are variants of evolutionary computation algorithms by organizing the population into grid structures, which are usually 2D grids. This paper proposes a new MOcGA, namely cosine multiobjective cellular genetic algorithm (C-MCGA), for continuous multiobjective optimization. The CMCGA introduces two new components: a 3D grid structure and a cosine crowding measurement. The first component is used to organize the population. Compared with a 2D grid, the 3D grid offers a vertical expansion of cells. The second one simultaneously considers the crowding distances and location distributions for measuring the crowding degree values for the solutions. The simulation results show that C-MCGA outperforms two typical MOcGAs and two state-of-the-art algorithms, NSGA-II and SPEA2, on a given set of test instances. Furthermore, the proposed measurement metric is compared with that in NSGA-II, which is demonstrated to yield a more diverse population on most of the test instances.

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Acknowledgments

This work was supported by the National Basic Research Program of China (Grant No. 2012CB821205), the Foundation for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 61021002), the National Natural Science Foundation of China (Grant No. 51275274, 61174037, and 61273313), and the Innovation Funds of China Academy of Space Technology (Grant No. CAST20120602). The authors would like to thank the anonymous reviewers for their insightful comments and constructive suggestions that have improved the paper.

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Authors and Affiliations

  1. Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, 150001, China

    Hu Zhang & Shenmin Song

  2. Department of Computer Science and Technology, East China Normal University, Shanghai, 200235, China

    Aimin Zhou

  3. Department of Electrical Engineering and Automation, Aalto University School of Electrical Engineering, Aalto, Finland

    X. Z. Gao

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  1. Hu Zhang
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  2. Shenmin Song
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  3. Aimin Zhou
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  4. X. Z. Gao
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Correspondence to Shenmin Song.

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Zhang, H., Song, S., Zhou, A. et al. A multiobjective cellular genetic algorithm based on 3D structure and cosine crowding measurement. Int. J. Mach. Learn. & Cyber. 6, 487–500 (2015). https://doi.org/10.1007/s13042-014-0277-6

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  • DOI: https://doi.org/10.1007/s13042-014-0277-6

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