A particle gradient evolutionary algorithm based on statistical mechanics and convergence analysis

  • Authors:

  • Kangshun Li;Wei Li;Zhangxin Chen;Feng Wang

  • Affiliations:

  • Jiangxi Univ. of Sci. and Techn., China and High-Performance Computing Techn. of Jiangxi Province, Jiangxi Normal Univ., China and Int. Comp. and Network Measurement-Control Techn. of Jiangxi Prov ...;School of Information Eng., Jiangxi Univ. of Sci. and Techn., Ganzhou, China and Key Lab. of Intelligent Computation and Network Measurement-Control Techn. of Jiangxi Province, Jiangxi Univ. of Sc ...;Center for Scientific Computation and Department of Mathematics, Southern Methodist University, Dallas, TX;Computer School of Wuhan University, Wuhan, China

  • Venue:
  • VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
  • Year:
  • 2006

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Abstract

In this paper a particle gradient evolutionary algorithm is presented for solving complex single-objective optimization problems based on statistical mechanics theory, the principle of gradient descending, and the law of evolving chance ascending of particles. Numerical experiments show that we can easily solve complex single-objective optimization problems that are difficult to solve by using traditional evolutionary algorithms and avoid the premature phenomenon of these problems. In addition, a convergence analysis of the algorithm indicates that it can quickly converge to optimal solutions of the optimization problems. Hence this algorithm is more reliable and stable than traditional evolutionary algorithms.