Artificial physics optimisation algorithm guided by diversity

  • Authors:

  • Gangjun Yang;Liping Xie;Ying Tan;Zhihua Cui

  • Affiliations:

  • Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, No. 66, Waliu Road, Wanbailin District, Taiyuan, Shanxi, 030024, China;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, No. 66, Waliu Road, Wanbailin District, Taiyuan, Shanxi, 030024, China;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, No. 66, Waliu Road, Wanbailin District, Taiyuan, Shanxi, 030024, China;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, No. 66, Waliu Road, Wanbailin District, Taiyuan, Shanxi, 030024, China

  • Venue:
  • International Journal of Computer Applications in Technology
  • Year:
  • 2013

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Abstract

In order to avoid the stagnation evolution of APO population, the thinking of dissipative structure theory and population diversity are combined in APO. Firstly, a chaos factor is introduced to judge whether the individuals doing dissipative movement or not, which is defined in a dissipation rule. However, the behaviour of an individual decided by the dissipation rule has blindness. Hence, population diversity is used to guide individual's movement. Then a diversity factor is introduced to judge whether population diversity is good or bad. If population diversity is worse than the diversity factor, individuals will do dissipative movement according to dissipation rule. The proposed algorithm is called APO algorithm guide by diversity APOD. Simulation results show APOD algorithm can improve the population diversity and global search capability of APO algorithm.