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A novel memetic algorithm and its application to data clustering

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Abstract

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.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    JiaCheng Ni, Li Li, Fei Qiao & QiDi Wu

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  1. JiaCheng Ni
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  2. Li Li
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  3. Fei Qiao
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  4. QiDi Wu
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Correspondence to JiaCheng Ni or Li Li.

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Ni, J., Li, L., Qiao, F. et al. A novel memetic algorithm and its application to data clustering. Memetic Comp. 5, 65–78 (2013). https://doi.org/10.1007/s12293-012-0087-x

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