Multi-objective Genetic Algorithm based on Game Theory and its Application

Mufti-objective optimization has been a difficult problem and focus for research in fields of science and engineering. There already have a lot of classical methods for solving mufti-objective optimization problems before evolutionary algorithms were introduced in 1985. Classical mufti-objective optimization methods have been thoroughly developed, but there are still Lots of shortcomings in solving high dimension, multimodal problems. GAs can handle large space of problem and get a lot of trade-of fronts (possible solutions) in one evolution. A GA does not need much information about the problem before starting the optimization process, also it is not sensitive to the convex of the defined fields of the objective functions. So using GAs in solving mufti-objective optimization problems is the most important research direction in the future. We import knowledge of immune, co-evolution and game theory into genetic algorithm to improve the performance on solving the mufti-objective optimization problems. The results of the riments show that all of them can get better results than the original algorithm.