A New Dynamical Evolutionary Algorithm Based on Particle Transportation Theory

Kangshun Li (), Yuanxiang Li, Zhangxin Chen () and Zhijian Wu
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Kangshun Li: Wuhan University, State Key Laboratory of Software 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

A chapter in Current Trends in High Performance Computing and Its Applications, 2005, pp 81-92 from Springer

Abstract: 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.

Keywords: Phase Space; Evolutionary Algorithm; Global Optimal Solution; Helmholtz Free Energy; Cyclic Code (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-27912-9_8

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DOI: 10.1007/3-540-27912-1_8

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