A novel memetic algorithm and its application to data clustering

作者：Ni, JiaCheng^*; Li, Li; Qiao, Fei; Wu, QiDi

来源：Memetic Computing, 2013, 5(1): 65-78.

DOI：10.1007/s12293-012-0087-x

• PSO
• Memetic algorithm
• Gaussian mutation
• SA-based local search

In this paper, a novel memetic algorithm (MA) named GS-MPSO is proposed by combining a particle swarm optimization (PSO) with a Gaussian mutation operator and a Simulated Annealing (SA)-based local search operator. In GS-MPSO, the particles are organized as a ring lattice. The Gaussian mutation operator is applied to the stagnant particles to prevent GS-MPSO trapping into local optima. The SA-based local search strategy is developed to combine with the cognition-only PSO model and perform a fine-grained local search around the promising regions. The experimental results show that GS-MPSO is superior to some other variants of PSO with better performance on optimizing the benchmark functions when the computing resource is limited. Data clustering is studied as a real case study to further demonstrate its optimization ability and usability, too.

• 出版日期2013-3
• 单位同济大学