A Fast Multi-Objective Genetic Algorithm Based on Clustering

Multi-objective genetic algorithm based on Pareto optimum is much suitable for solving multi-objective optimization problems. In this paper,the relations between individuals and some features about these relations are discussed. It is proved that the individuals of an evolutionary population can be classified by the idea of quick sort. At the same time,the approach to maintain diversity of solutions by clustering algorithms is discussed,and the clustering algorithm based on hierarchical aggregation is also discussed. Then by using the quick sort algorithm and the clustering procedure,an algorithm of constructing a new evolutionary population is proposed. It is shown by theoretic analysis and experimental results that the convergent speed of the algorithm discussed is more efficient than the other existing algorithms.