Research and Application on Job Shop Planning Based on Improved Glowworm Swarm Optimization Algorithm

In order to complete the planning problem under the specific environment, an improved Artificial Glowworm Swarm Optimization (GSO) algorithm is proposed. In this algorithm, exchange and mutation is performed after each iterative. After each exchange and mutation the brightness of firefly, which related to fitness function value positively, is calculated and compared with brightness that has been got before this action for deciding whether to change the location of the firefly. Finally, most fireflies will gather on the location where the fitness function value is best. A job planning model Based on the improved GSO algorithm is built by analysing the production process synthetically for the actual model of tobacco and the algorithm design is also given. Finally, the simulation is done and results show the improved GSO algorithm has good feasibility in tobacco production.