An ACO-Based Clustering Algorithm With Chaotic Function Mapping

Lei Yang, Xin Hu, Hui Wang, Wensheng Zhang, Kang Huang and Dongya Wang
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Lei Yang: South China Agricultural University, China
Xin Hu: South China Agricultural University, China
Hui Wang: South China Agricultural University, China
Wensheng Zhang: Chinese Academy of Sciences, China
Kang Huang: South China Agricultural University, China
Dongya Wang: University of Exeter, UK

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2021, vol. 15, issue 4, 1-21

Abstract: To overcome shortcomings when the ant colony optimization clustering algorithm (ACOC) deal with the clustering problem, this paper introduces a novel ant colony optimization clustering algorithm with chaos. The main idea of the algorithm is to apply the chaotic mapping function in the two stages of ant colony optimization: pheromone initialization and pheromone update. The application of chaotic mapping function in the pheromone initialization phase can encourage ants to be distributed in as many different initial states as possible. Applying the chaotic mapping function in the pheromone update stage can add disturbance factors to the algorithm, prompting the ants to explore new paths more, avoiding premature convergence and premature convergence to suboptimal solutions. Extensive experiments on the traditional and proposed algorithms on four widely used benchmarks are conducted to investigate the performance of the new algorithm. These experiments results demonstrate the competitive efficiency, effectiveness, and stability of the proposed algorithm.

Date: 2021
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