A parallel artificial bee colony algorithm for the job shop scheduling problem with a dynamic migration strategy

- A parallel ABC (pABC) algorithm proposed to solve the job shop scheduling problem.
- The pABC algorithm consists of several colonies that evolve in parallel manner.
- A dynamic migration strategy is used to determine when colonies must communicate.
- Results showed that the pABC algorithm improves the solution quality.
- The results also showed that the convergence speed in the pABC algorithm is very high.

The job shop scheduling problem (JSSP) has attracted much attention in the field of both information sciences and operations research. Many heuristic algorithms such as genetic algorithms, particle swarm optimization, artificial bee colony algorithm, and ant colony optimization have been presented to solve it. Parallelization is one of the best approaches that can be used to enhance the performance of these heuristic algorithms. In this paper, we propose a parallel artificial bee colony algorithm to solve the job shop scheduling problem. In this method, artificial bee colony algorithm consists of several colonies that locate on different hosts of the network and algorithm is carried out in various colonies in parallel manner. The communication between colonies is carried out by exchanging migrants. A dynamic migration strategy is used to determine when a colony must communicate by its neighbors. Benchmark instances are used to investigate the performance of the proposed approach. The results show that the proposed parallel artificial bee colony algorithm improves the efficiency.