Author: David Chui

In PSO, the population size refers to the number of particles (or individuals) within the swarm. It has significant impact on speed of convergence and quality of solution. Convergence Speed Larger population sizes tend to lead to faster convergence because of the coverage of the search space. However, the convergence speed diminishes as the population…

In Particle Swarm Optimisation (PSO), acceleration coefficients c1 and c2 play crucial roles in influencing the cognitive and social components, respectively. These components represent the individual and collective learning aspects of PSO, contributing to the balance between local and global search. The cognitive component (c1) represents each particle’s individual learning, emphasising the importance of personal…

The inertia weight (w) in Particle Swarm Optimisation (PSO) is a crucial parameter that significantly impacts the algorithm’s efficiency. It plays a pivotal role in balancing exploration and exploitation during the optimisation process. Exploration meaning searching in the global space. Exploitation meaning searching within the local space. Essentially, the inertia weight regulates the particle’s tendency…

In conclusion, understanding the interplay between Population Size, Acceleration Coefficients (c1 and c2), and Inertia Weight (w) is crucial for effectively using PSO in solving optimisation problems. By fine-tuning these parameters based on problem characteristics and optimisation objectives, scientists can enhance PSO’s convergence speed and solution quality across a wide range of applications. In other…