Particle Swarm Optimization with Velocity Restriction and Evolutionary Parameters Selection for Scheduling Problem

TL;DR

This paper enhances Particle Swarm Optimization for scheduling by restricting particle velocity and using genetic algorithms for parameter tuning, demonstrating improved performance on job-shop scheduling tasks.

Contribution

It introduces a velocity restriction and an evolutionary parameter selection method for PSO, improving its effectiveness in scheduling problems.

Findings

Velocity restriction improves PSO convergence.

Genetic algorithms effectively tune PSO parameters.

Enhanced PSO outperforms standard methods on test tasks.

Abstract

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.