Multi-Swarm Optimization (MSO) is a variant of the Particle Swarm Optimization (PSO) algorithm. It improves upon PSO by utilizing multiple sub-swarms instead of one, each evolving independently and occasionally sharing information, enabling exploration of different areas of the solution space.
Picture a group of birds searching for food. If they all flock together, they can only search one area at a time. But if they split up into smaller groups and go to different places, they can cover more ground. Occasionally, these groups might communicate with each other to share where they found the most food. This is exactly what Multi-Swarm Optimization does. It’s a smart way to solve problems by searching in different places at the same time.
In simple terms, Multi-Swarm Optimization (MSO) is an optimization algorithm inspired by the social behavior of bird flocking or fish schooling. More specifically, it is an extension of the Particle Swarm Optimization (PSO), a heuristic optimization method created by Kennedy and Eberhart in 1995. The main difference is that instead of using a single swarm (a group of potential solutions), MSO leverages multiple sub-swarms exploring different parts of the solution space.
Each particle represents a potential solution in the search space, and its movement is guided by its own experience and that of its neighbors. In the MSO, these particles are separated into different sub-swarms, each evolving according to its own dynamics, which allows for a more refined search as each swarm might explore a different region of the search space.
Periodically, these sub-swarms exchange particles, benefiting from the exploration and exploits of other sub-swarms. This exchange strategy promotes diversity among particles, mitigating problems like early convergence or getting stuck in local optima, which are common problems in optimization.
So, MSO is an iterative process that adjusts the position of individual particles within different sub-swarms based on their own and their sub-swarms’ best performances. The result of an MSO algorithm is the best solution found over the course of iterations.
MSO finds extensive use in diverse disciplines such as machine learning, deep learning, feature selection, and more, where complex optimization problems often occur.