Hybrid Global Optimization Algorithm for Feature Selection

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm (PLTVACIW-PSO). Its designed has introduced the benefits of Parallel computing into the combined power of TVAC (Time-Variant Acceleration Coefficients) and IW (Inertial Weight). Proposed algorithm has been tested against linear, non-linear, traditional, and multiswarm based optimization algorithms. An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO. Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWential evolution (DE), and, finally, Flower Pollination (FP) algorithms. In phase II, the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT (BA) and Multi-Swarm BAT algorithms. In phase III, the proposed PLTVACIW-PSO is employed to augment the feature selection problem for medical datasets. This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms. Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.