Hybrid Particle Swarm Optimization with Ant-Lion Optimization: Experimental in Benchmarks and Applications
نویسندگان
1 Department of Computer Science, Kosar University of Bojnord, Iran.
2 Department of Applied Mathematics and Computer Science, University of Isfahan, P.O. Box 81746,73441, Isfahan, Iran, University.
doi
10.22044/jadm.2021.10550.2195چکیده
A major pitfall in the standard version of Particle Swarm Optimization (PSO) is that it might get stuck in the local optima. To escape this issue, a novel hybrid model based on the combination of PSO and AntLion Optimization (ALO) is proposed in this study. The proposed method, called H-PSO-ALO, uses a local search strategy by employing the Ant-Lion algorithm to select the less correlated and salient feature subset. The objective is to improve the prediction accuracy and adaptability of the model in various datasets by balancing the exploration and exploitation processes. The performance of our method has been evaluated on 30 benchmark classification problems, CEC 2017 benchmark problems, and some well-known datasets. To verify the performance, four algorithms, including FDR-PSO, CLPSO, HFPSO, MPSO, are elected to be compared with the efficiency of H-PSO-ALO. Considering the experimental results, the proposed method outperforms the others in many cases, so it seems it is a desirable candidate for optimization problems on real-world datasets.
