A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics

In quantitative structure-activity relationship (QSAR) classification, descriptor selection is one of the most important topics in the chemometrics. The selection of descriptors can be considered to be a variable selection problem that aims to find a small subset of descriptors that has the most discriminative information for the classification target. Penalized support vector machine (PSVM) proved its effectiveness by creating a strong classifier that combines the advantages of the support vector machine and the penalization. PSVM with Ll-norm and smoothly clipped absolute deviation (SCAD) penalty is the most widely used methods. However, the efficiency of PSVM with these penalties depends on appropriately choosing the tuning parameter which is involved in both penalties. Hybrid metaheuristics algorithms are of the most interesting recent trends in optimization to escape from the trapped in the local optimal. In this paper, a new hybrid firefly algorithm and particle swarm optimization is proposed to determine the tuning parameter in PSVM. Our proposed algorithm can efficiently exploit the strong points of both firefly and particle swarm algorithms in finding the most relevant descriptors with high classification performance. The experimental results on four benchmark QSAR datasets show the superior performance of the proposed algorithm in terms of classification accuracy and the number of selected descriptors compared with other competitor methods.