Metaheuristic methods in hybrid flow shop scheduling problem

Memetic algorithms are hybrid evolutionary algorithms that combine global and local search by using an evolutionary algorithm to perform exploration while the local search method performs exploitation. This paper presents two hybrid heuristic algorithms that combine particle swarm optimization (PSO) with simulated annealing (SA) and tabu search (TS), respectively. The hybrid algorithms were applied on the hybrid flow shop scheduling problem. Experimental results reveal that these memetic techniques can effectively produce improved solutions over conventional methods with faster convergence.

► The proposed PSO has a competitive performance as compared to GA and ACS algorithms and superior performance when compared to TS.
► The proposed hybrid PSO-SA provided the best results and improved the performance of PSO significantly.
► In terms of effort to develop an algorithm, execution time of algorithm and simplicity to perform fine-tuning, PSO tops all the other metaheuristics.
► Memetic techniques can effectively produce improved solutions over conventional methods with faster convergence.