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 to persist in its current direction. A higher inertia weight promotes exploration, while a lower inertia weight facilitates exploitation.
Varying Values of Inertia Weight (w)
High Inertia Weight (w):
- Advantages:
- Promotes global exploration by maintaining high velocities of particles
- Helps escape local optima and explore diverse regions of the search space.
- Challenges:
- May lead to excessive exploration, slowing down convergence in the later stages of optimization.
- Risk of overshooting, resulting in oscillatory behaviour.
Low Inertia Weight (w):
- Advantages:
- Facilitates local exploitation by reducing particle velocities.
- Helps converge towards promising regions of the search space, improving solution quality.
- Challenges:
- May get trapped in local optima, limiting the algorithm’s ability to explore the entire space.
- Prone to premature convergence, especially in rugged or multimodal search spaces.
Try it Yourself!
Experimental Results:
Inertia weight significantly affects the convergence speed of the PSO algorithm. High inertia weights tend to yield faster initial convergence due to increased exploration but may slow down convergence later on. In contrast, low inertia weights often lead to slower but more stable convergence, with improved solution quality. Still, the optimal inertia weight varies depending on factors such as problem complexity, search space characteristics, and desired convergence behaviour.