An improved thermodynamics evolutionary algorithm based on the minimal free energy

  • Authors:

  • Fahong Yu;Yuanxiang Li;Weiqin Ying

  • Affiliations:

  • State Key Lab of Software Engineering, Wuhan University, Wuhan, China;State Key Lab of Software Engineering, Wuhan University, Wuhan, China;State Key Lab of Software Engineering, Wuhan University, Wuhan, China

  • Venue:
  • ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
  • Year:
  • 2010

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Abstract

In this paper, an improved thermodynamics evolutionary algorithm (ITEA) is proposed The purpose of the new algorithm is to systematically harmonize the conflict between selective pressure and population diversity while searching for the optimal solutions ITEA conforms to the principle of minimal free energy in simulating the competitive mechanism between energy and entropy in annealing process, in which population diversity is measured by similarity entropy and the minimum free energy is simulated with an efficient and effective competition by free energy component Through solving some typical numerical optimization problems, satisfactory results were achieved, which showed that ITEA was a preferable algorithm to avoid the premature convergence effectively and reduce the cost in search to some extent.