Introduction to Particle Swarm Optimization (PSO) using JavaScript
In the realm of optimization algorithms, Particle Swarm Optimization (PSO) stands out as an efficient and intuitive technique inspired by social behaviour. Developed in the 1990s by Eberhart and Kennedy, PSO has gained popularity in various fields, including engineering, computer science, and economics. By simulating the movement of a swarm of particles, PSO searches for optimal solutions in complex problem spaces. In this post, we will delve into the essence of PSO, employing a relatable metaphor to facilitate understanding, and present a basic implementation in JavaScript. Understanding PSO through the Flock of Birds Metaphor To grasp the concept of PSO, let's imagine a flock of birds searching for the most favourable location to roost for the night. Each bird represents a potential solution to the optimization problem. The goal is to find the optimal location, analogous to the solution we seek. Initially, the birds scatter randomly across the sky. However, they are not entirely ...