A New Particle Dynamical Evolutionary Algorithm for Solving Complex Constrained Optimization Problems

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 non-equilibrium. 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.