Particle Swarm Optimization (PSO) is a stochastic optimization approach inspired by the behavior of flocks of birds or swarms of insects. It solves problems by having a population of candidate solutions, here called particles, and moving these particles around the search space according to simple mathematical formulae.
Imagine you and your friends are playing a game in a big park where you have to find a treasure. Each of you starts looking in different places. If a friend finds a clue, they shout, and everyone moves closer to that friend, still looking around. You all keep doing this—calling others over when you find a good clue—until finally one of you finds the treasure. Particle Swarm Optimization works just like this game, but instead of you and your friends, there are ‘particles’, and the treasure they’re looking for is the best solution to a problem.
Particle Swarm Optimization is a paradigm of swarm intelligence, a subfield of AI inspired by the collective behaviors of social colonies or swarms such as birds, ants, or bees. It is essentially a stochastic, population-based optimization algorithm designed to locate the best solutions in a multidimensional search space.
At the beginning of the PSO algorithm, a swarm of particles is randomly initiated in the design space, each representing a potential solution. Each particle keeps track of its coordinates in the problem space which are associated with the best solution (fitness) it has achieved so far. This value is called ‘personal best’ or ‘pbest’.
Another best value tracked by the global version of PSO is the best solution (fitness) obtained so far by any particle in the whole population. This globally best value is called ‘global best’ or ‘gbest’.
In each iteration of the algorithm, for every particle in the swarm, new velocities towards the pbest and gbest positions are calculated using certain formulae. This velocity determines the next position of the particle in the design space. Hence, the swarm gradually converges towards the regions of the search space with the highest fitness.
The algorithm balances exploration (searching a larger area for alternative solutions) with exploitation (zooming in around a currently known best solution). This balance is key to helping avoid getting stuck in suboptimal solutions or endlessly exploring without converging to an answer, common pitfalls in other optimization methods.
The PSO algorithm is widely applied in various fields such as function optimization, neural network training, and fuzzy system control due to its simplicity of implementation, computational efficiency, and robustness.