A MOP Evolutionary Algorithm Based on Transportation Theory
Kang Li · Chinese Journal of Computers · 2007
In this paper a Multi-objective Optimization Problems Evolutionary Algorithm,MOPEA,for solving multi-objective optimization problems precisely and efficiently is presented according to the equation of particle transportation and the principle of energy decreasing and the law of entropy increasing in phase space of particles based on transportation theory.In the algorithm,the theory of particle system changing from non-equilibrium to equilibrium is used to define the Rank function and Niche function for solving multi-objective problems,all the individuals in the population have chance to participate the evolving operation to solve the Pareto optimal solutions of the multi-objective problems fast and evenly.The experiments show that this algorithm can not only converge to global Pareto optimal front fast and precisely,but also can avoid premature phenomenon of multi-objective problems because the algorithm requires all the particles in the phase space to cross and mutate simultaneously.Through analyzing the performance indices of evolutionary algorithms it illustrates that this algorithm have more advantages than traditional evolutionary algorithms.