A novel constraint multi-objective artificial physics optimisation algorithm and its convergence

  • Authors:

  • Yan Wang;Jian-/Chao Zeng;Zhi-/Hua Cui;Xiao-/Juan He

  • Affiliations:

  • College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China/ Complex System and Computational Intelligence Laboratory, Taiyuan University of Science ...;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, 030024, China.;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, 030024, China.;College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China/ Complex System and Computational Intelligence Laboratory, Taiyuan University of Science ...

  • Venue:
  • International Journal of Innovative Computing and Applications
  • Year:
  • 2011

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Abstract

This paper presents a constraint multi-objective artificial physics optimisation (CMOAPO) algorithm by introducing a novel optimisation paradigm called artificial physics optimisation (APO) into constraint multi-objective domain. Combining with characteristics of constraint multi-objective optimisation problems, a method of virtual force decreasing is incorporated into CMOAPO to decrease the probability of individuals moving from feasible region into infeasible region. Furthermore, the convergence of CMOAPO is analysed in terms of theory with related knowledge of probability. The performance of CMOAPO algorithm is tested using several benchmark functions. The results obtained show that the proposed approach is effective.