In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
PSO can be applied for various optimization problems, for example, Energy-Storage Optimization. Moreover, since PSO can simulate the movement of a particle swarm, this can also be applied to movie film as shown in this figure.
Particle swarm optimization (PSO) is a population based stochastic optimizationtechnique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by socialbehavior of bird flocking or fish schooling for solving continuous and discreteoptimization problems.
Consider the following scenario: a group of birds are randomly searching food inan area. There is only one piece of food in the area being searched. All the birdsdo not know where the food is. But they know how far the food is in eachiteration. So what's the best strategy to find the food? The effective one is tofollow the bird which is nearest to the food.
PSO learned from the above scenario and used it to solve the optimization problems. InPSO, each single solution is a "bird" in the search space. We call it "particle". All ofparticles have fitness values which are evaluated by the fitness function to be optimized,and have velocities which direct the flying of the particles. The particles fly through theproblem space by following the current optimum particles.
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