A hybrid genetic-particle swarm algorithm based on multilevel neighbourhood structure for flexible job shop scheduling problem

Reasonable job shop scheduling can improve the production efficiency and product delivery and reduce the costs and energy consumption. The quality of a scheduling scheme mainly depends on the performance of the used algorithm. Therefore, several researchers have attempted to improve the performance of algorithms used for solving the flexible job shop scheduling problem (FJSSP). Currently, the genetic algorithm (GA) is one of the most widely used algorithms for solving the FJSSP. However, it has a low convergence speed and accuracy. To overcome these limitations of the GA, a novel variable neighbourhood descent hybrid genetic algorithm (VNDhGA) is proposed here. In this algorithm, a barebones particle swarm optimisation (BBPSO)-based mutation operator, a hybrid heuristic initialisation strategy, and VND based on an improved multilevel neighbourhood structure are integrated into the standard GA framework to improve its convergence performance and solution accuracy. Furthermore, a real-number-based chromosome representation, coding, decoding, and crossover method is proposed for maximising the advantages of BBPSO. The proposed algorithm was tested on benchmark cases, and the results were compared with those of existing algorithms. The proposed algorithm exhibited superior solution accuracy and convergence performance than those of existing ones.