An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling

Enhanced Particle Swarm Optimization

Particle Swarm Optimization with Transition Probability for Timetabling Problems

In this paper, we propose a new algorithm to solve university course timetabling problems using a Particle Swarm Optimization (PSO). PSOs are being increasingly applied to obtain near-optimal solutions to many numerical optimization problems. However, it is also being increasingly realized that PSOs do not solve constraint satisfaction problems as well as other meta-heuristics do. In this paper...

Solving effectively the school timetabling problem using particle swarm optimization

New results are listed below, derived from the algorithm presented at the corresponding paper, due to a minor significance bug correction and further experiments conducted after the publication date of the paper. The new results may differ from those mentioned at the text of the original paper. Nevertheless, the new results are, in the great majority, at least as good as the previous ones conce...

An approach to Improve Particle Swarm Optimization Algorithm Using CUDA

The time consumption in solving computationally heavy problems has always been a concern for computer programmers. Due to simplicity of its implementation, the PSO (Particle Swarm Optimization) is a suitable meta-heuristic algorithm for solving computationally heavy problems. However, despite the simplicity, the algorithm is inefficient for solving real computationally heavy problems but the pr...

Particle swarm optimization An overview

Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors’ perspective, includ...