Job-shop Scheduling Problem with Improved Lion Swarm Optimization

In view of the shortcomings of the basic lion swarm optimization, which is prone to local optimality and low convergence accuracy in partial optimization, this paper proposes a lion swarm optimization based on chaotic search and gaussian perturbation. The improved algorithm adds chaos search and gaussian perturbation strategy to the position of lions in the past dynasties, which improves the optimization efficiency of the algorithm in the optimization process. The simulation results of the test function show that the optimization accuracy of the improved algorithm is much higher than that of the basic lion swarm optimization. The improved algorithm effectively prevents the swarm optimization from easily falling into the local optimal value in the extremely difficult optimization function. Finally, an example of job-shop scheduling problem with the goal of minimizing the total time of job processing is tested. The test results verify the effectiveness of the algorithm.