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| A New Particle Dynamical Evolutionary Algorithm for Solving Complex Constrained Optimization Problems |
| LI KangShun1,2,3, LI YuanXiang2, KANG LiShan2, LI BangHe4 |
1.School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000
2.State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072
3.Provincial Key Laboratory of HighPerformance Computing Technology, Jiangxi Normal University, Nanchang 341000
4.Institute of Mathematics and System Science, Chinese Academy of Sciences, Beijing 100080 |
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Abstract In this paper a particle dynamic evolutionary algorithm(CPDEA) for solving constrained problems efficiently is presented according to the equation of particle transportation, the principle of energy minimizing and the law of entropy increasing in phase space of particles based on transportation theory. A fitness function of constrained optimization problems is defined based on the theory in which particle systems in phase space reach equilibrium from nonequilibrium. The energy of particle systems minimizes and the entropy of particle systems increases gradually in the evolving process of particles in order that all the individuals have chance to crossover and mutate. Finally all the optimal solutions are obtained quickly. In the numerical experiments, precise optimal solutions of the constrained problems are gotten by using this algorithm. Compared with traditional evolutionary algorithms, the experiments show that not only all the global solutions of complex constrained optimization problems can be solved in an easy and quick way, but also premature phenomenon can be avoided.
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Received: 17 September 2004
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2006, Vol. 19 
