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Abstract
In this article, we propose a fast annealing genetic algorithm (FAGA), based on the principle of the minimal free energy in statistical physics, for solving multi-objective optimization problems. The novelties of FAGA are: Equation(1) providing a new fitness assignment strategy by combining Pareto-dominance relation and Gibbs entropy, Equation(2)
introducing a new criterion for selection of new individuals to maintain the diversity of the population. We make many experiments to measure the performance of the proposed FAGA, and estimate its convergence rate for a number of test problems. Simulation results show that the FAGA is a very fast and effective algorithm.
Acknowledgements
We would like to thank Mr. Naoki Mori in Osaka Prefecture University, Japan for providing us their valuable papers. This research was supported by the National Natural Science Foundation of China (No. 60473081), and the Natural Science Foundation of Hubei Province (No. 2004ABA011).