A hybrid computational chemotaxis in bacterial foraging optimization algorithm for global numerical optimization

This paper first proposes a simple scheme for adapting the chemotactic step size of the Bacterial Foraging Optimization Algorithm (BFOA), and then this new adaptation and two very popular optimization techniques called Particle Swarm Optimization (PSO) and Differential Evolution (DE) are coupled in a new hybrid approach named Adaptive Chemotactic Bacterial Swarm Foraging Optimization with Differential Evolution Strategy (ACBSFO _DES). This novel technique has been shown to overcome the problems of premature convergence and slow of both the classical BFOA and the other BFOA hybrid variants over several benchmark problems.