Enhanced PSO based on population initialization and exploration for the permutation flow shop scheduling problem

Abstract EN

In this paper, a hybrid PSO (NLPSO*) is adapted to improve the obtained local optimal solution for the Permutation Flow Shop Scheduling Problem (PFSP) with minimizing the makespan.

In this method, an improved NEH heuristic called ?-NEH+ is used to generate a good initial population.

Then the PSO is triggered, followed by Iterated Local Search (ILS) to increase the coverage of exploration and exploitation search in the solution space.

Both of the ?-NEH+ and ILS are simple and efficient algorithms.

A computational study is performed to show the efficiency of the proposed technique.

Several of well-known PFSP instances of small, medium, and large sizes were used in this study.

The experimental study shows that the NLPSO* algorithm is significantly efficient in reaching better local optimal solutions.