关键词: 模糊聚类, 多目标优化, 萤火虫算法, 变异, 差分进化

Abstract:

It has been shown that most traditional fuzzy clustering algorithms only optimize a single objective function, hence more comprehensive and accurate clustering result cannot be achieved. To solve this problem, a new fuzzy clustering technique based on improved multi-objective Firefly Algorithm (FA) was proposed. Firstly, a mutation mechanism with dynamically decreasing probability which was similar to the mutation operator in Differential Evolution (DE) algorithm was drawn into FA, in order to obtain more uniformly distributed non-dominated solutions, simultaneously the scaling factor was adaptively adjusted to enhance the efficiency of mutation. When the archive was filled, some solutions in it were selected to combine with the current population for the next evolution to improve the efficiency of the algorithm. Finally, this algorithm was applied to fuzzy clustering problem, which simultaneously optimized two objectives of fuzzy clustering index, and one solution was selected from the final archive to get the result of clustering. The experimental results on five groups of data show that the proposed algorithm raises the clustering validity index by 2 to 8 percentages than traditional single objective clustering algorithm, so it can achieve higher accuracy of clustering and obtains better comprehensive performance.

Key words: fuzzy clustering, multi-objective optimization, Firefly Algorithm (FA), mutation, Difference Evolution (DE)