Test-Case Prioritization Using Binary Particle Swarm Optimization Method

Particle swarm optimization method is based on artificial intelligence technique. It is an optimization method that was developed in 1995 by Eberhart and Kennedy based on the social behaviors of fish schooling or birds flocking. By increasing the overall rate of fault detection, a greater number of errors can be found more rapidly in the code. Particle , fitness function , local best , global best , velocity update , position update are the commonly used elements in particle swarm optimization.PSO algorithms have been developed to solve constrained problems, multi-objective optimization problems, problems with dynamically changing landscapes, and to find multiple solutions. On the other hand some of them defined different methods like inertia weight to improve the performance of PSO. KeywordsOptimization, Prioritization, artificial intelligence, Swarm, inertia, constriction.