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A New Dynamical Evolutionary Algorithm Based on Particle Transportation Theory

In: Current Trends in High Performance Computing and Its Applications

Author

Listed:
  • Kangshun Li

    (Wuhan University, State Key Laboratory of Software Engineering
    Jiangxi University of Science & Technology, School of Information Engineering)

  • Yuanxiang Li

    (Wuhan University, State Key Laboratory of Software Engineering)

  • Zhangxin Chen

    (Southern Methodist University, Center for Scientific Computation and Department of Mathematics)

  • Zhijian Wu

    (Wuhan University, State Key Laboratory of Software Engineering)

Abstract

In this paper, a new dynamical evolutionary algorithm is presented based on a particle transportation theory according to the principle of energy minimization and the law of entropy increasing in the phase space of particles. In numerical experiments we use this algorithm to solve optimization problems, which are difficult to solve using traditional evolutionary algorithms (e.g., the minimization problem of six-hump camel back functions). Compared with the traditional evolutionary algorithms, this new algorithm not only solves linear and nonlinear optimization problems more quickly, but also more easily finds all the points that reach the global solutions of these problems because it drives almost all the individuals to have chances to participate in crossing and mutating. The results of numerical experiments show that this dynamical evolutionary algorithm obviously improves the computing performance of the traditional evolutionary algorithms in that its convergent speed is faster and it is more reliable.

Suggested Citation

  • Kangshun Li & Yuanxiang Li & Zhangxin Chen & Zhijian Wu, 2005. "A New Dynamical Evolutionary Algorithm Based on Particle Transportation Theory," Springer Books, in: Wu Zhang & Weiqin Tong & Zhangxin Chen & Roland Glowinski (ed.), Current Trends in High Performance Computing and Its Applications, pages 81-92, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-27912-9_8
    DOI: 10.1007/3-540-27912-1_8
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