An Improved Multi-Objective Genetic Algorithm Based On Pareto Front and Fixed Point Theory

摘要

For multi-objective optimization problems, an improved multi-objective genetic algorithm based on Pareto Front and Fixed Point Theory is proposed in this paper. In this Algorithm, the fixed point theory is introduced to multi-objective optimization questions and K₁ triangulation is carried on to solutions for the weighting function constructed by all sub- functions, so the optimal problems are transferred to fixed point problems. The non-dominated-set is constructed by the method of exclusion. The experimental results show that this improved genetic algorithm convergent faster and is able to achieve a broader distribution of the Pareto optimal solution.