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 blog posts, only the Ackley function was used to demonstrate PSO performance under different parameters. However, it is mentioned often that different functions require different parameter settings. In this post, the PSO simulation will be presented with more than 1 objective functions and variable parameters to test out the limits of PSO.
Again, feel free to play with it! But beware of computational overhead, the simulation gets really slow.
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