An investigation of multi-objective particle swarm optimization algorithm for better performance and its application to signalized traffic problem

Final Year Project/Work

S&T Best Computing Projects

Bachelor of Computing with Honours in Internet Technology

2020

In this project, we are going to study and improve the state-of-the-art MOPSO algorithms. The proposed algorithm was based on the competitive mechanism MOPSO that guides the particles by the current population. We aim to achieve better performance that balances between exploration and exploitation of the whole swarm, avoid premature convergence, and maintain a well-distributed Pareto front. The proposed algorithm was applied to the signalized traffic problem to optimize the effective green time of each phase in real-world problems.

Mixed

English

This work includes 1 PDF file and 1 video clip.

traffic congestion; traffic signs and signals; swarm intelligence; computational intelligence; mathematical optimization; multiple criteria decision making

Public Access

An investigation of multi-objective particle swarm optimization algorithm for better performance and its application to signalized traffic problem - video

S&T Best Computing Projects

Video

3 minutes 54 seconds.

Public Access