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A multi-objective artificial physics optimization algorithm based on ranks of individuals

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Abstract

This paper proposes a multi-objective artificial physics optimization algorithm based on individuals’ ranks. Using a Pareto sorting based technique and incorporating the concept of neighborhood crowding degree, evolutionary individuals in the search space are evaluated at first. Then each individual is assigned a unique serial number in terms of its performance, which affects the mass of the individual. Thereby, the population evolves towards the direction of the Pareto-optimal front. Synchronously, the presented approach has good diversity, such that the population is spread evenly on the Pareto front. Results of simulation on a number of difficult test problems show that the proposed algorithm, with less evolutionary generations, is able to find a better spread of solutions and better convergence near the true Pareto-optimal front compared to classical multi-objective evolutionary algorithms (NSGA, SPEA, MOPSO) and to simple multi-objective artificial physics optimization algorithm.

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Acknowledgments

The authors gratefully acknowledge the comments from the anonymous reviewers, which greatly helped them to improve the contents of this paper. The authors acknowledge support from the fund of the scholarship to pursue graduated studies in Taiyuan University of Science and Technology and the project number is 20122030.

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Authors and Affiliations

  1. Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, 030024, People’s Republic of China

    Yan Wang & Jian-chao Zeng

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  1. Yan Wang
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  2. Jian-chao Zeng
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Correspondence to Yan Wang.

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Communicated by A-A. Tantar.

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Wang, Y., Zeng, Jc. A multi-objective artificial physics optimization algorithm based on ranks of individuals. Soft Comput 17, 939–952 (2013). https://doi.org/10.1007/s00500-012-0969-3

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