A comparative study of four metaheuristics applied for solving the flow-shop scheduling problem

The Flow shop-scheduling problem is NP-hard combinatorial optimization problem, thus, it requires using the computational intelligence to solve it. This paper describes an experimental comparison study of four metaheuristics which are the hybrid genetic algorithm, particle swarm optimization (by and without using local search), and the cat swarm optimization algorithm, in order to analyze their performance in term of solution. The four algorithms has been applied to some benchmark Flow shop problems. The results show that the Cat swarm optimization algorithm is more efficient than other methods to solve the flow shop-scheduling problem.