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