Particle Swarm Optimization with Velocity Restriction and Evolutionary Parameters Selection for Scheduling Problem
This incremental improvement addresses scheduling optimization for industrial or computational applications.
The paper tackled the job-shop scheduling problem by enhancing Particle Swarm Optimization with velocity restriction and evolutionary parameter selection using Genetic Algorithms, achieving improved performance as demonstrated in experiments.
The article presents a study of the Particle Swarm optimization method for scheduling problem. To improve the method's performance a restriction of particles' velocity and an evolutionary meta-optimization were realized. The approach proposed uses the Genetic algorithms for selection of the parameters of Particle Swarm optimization. Experiments were carried out on test tasks of the job-shop scheduling problem. This research proves the applicability of the approach and shows the importance of tuning the behavioral parameters of the swarm intelligence methods to achieve a high performance.