Discrete optimization problem solving with three variants of hybrid binary particle swarm optimization

Binary Particle Swarm Optimization (BPSO) is a population based stochastic algorithm for discrete optimization inspired by social behavior of bird flocking or fish schooling that has been successfully applied in different areas. However, its potential has not been sufficiently explored. Recent works have proposed hybridization of BPSO with promising results. This paper aims to present three variants of hybrid BPSO algorithm, which is differently to the previous approaches. This work, maintains the main BPSO concept for the update of the velocity of the particle and position, one additional step is added to the method that is crossover technique of Genetic Algorithm. The paper describes the three proposed algorithms and a set of experiments with the standard benchmark functions. The hybrid algorithm shows competitive results compared to Classical BPSO.