Comparison of two variants of particle swarm optimization algorithm for solving flexible job shop scheduling problem

The Scheduling is one of the most challenging problems faced in many different areas of everyday life. This problem can be formulated as a combinatorial optimization problem, and it has been solved with various methods using nature-inspired meta-heuristics and intelligent algorithms. We present in this paper a solution to the flexible ob shop scheduling problem using two variants of particle swarm optimization namely parametric version (PSO) and fully-adaptive one (TRIBES). TRIBES like PSO, is a computational method that mimics the behavior of flying birds and their means of information exchange. The candidate solutions in the swarm communicate and cooperate with each other, whereas individuals in an evolutionary algorithm compete for survival. A study comparing the performances of both solutions is described and the results are analyzed.