Bacterial Foraging Particle Swarm Optimization Algorithm Based Fuzzy-VQ Compression Systems

This study proposes a novel bacterial foraging swarm-based intelligent algorithm called the bacterial foraging particle swarm optimization (BFPSO) algorithm to design vector quantization (VQ)-based fuzzy-image compression systems. It improves compressed image quality when processing many image patterns. The BFPSO algorithm is an efficient evolutionary learning algorithm that manages complex global optimal codebook generation problems. The BFPSO algorithm combines bacterial foraging optimization (BFO) behavior with a particle swarm optimization (PSO) learning scheme to obtain fast convergence and self-adaptive learning benefits. The evolutionary BFPSO algorithm automatically designs appropriate parameters for fuzzy-VQ-based systems using a proper codebook selection machine. Computer simulation results of nonlinear image compression applications demonstrate the efficiency of the BFPSO learning algorithm. The differences between the proposed BFPSO learning scheme and the BFOand LBG-based VQ learning methods demonstrate the superior image results produced by the proposed algorithm.