Hybrid Particle Swarm Optimization with Ant-Lion Optimization: Experimental in Benchmarks and Applications

A major pitfall in the standard version of Particle Swarm Optimization (PSO) is that it might get stuck in the local optima. In order to escape this issue, a novel hybrid model based on a combination of PSO and Ant-Lion Optimization (ALO) is proposed in this work. 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 is evaluated on benchmark classification problems, CEC 2017 benchmark problems, and some well-known datasets.in order to verify the performance, four algorithms, including FDR-PSO, CLPSO, HFPSO, and 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 that it is a desirable candidate for the optimization problems on real-world datasets.