Particle Swarm Optimization (PSO) and its Applications

In this video, Prof. Cheng will introduce another algorithm and its applications: Particle Swarm Optimization (PSO).

Particle swarm optimization (PSO) is a robust evolutionary strategy inspired by the social behavior of animal species living in large colonies like birds, ants or fish. Prof. Cheng will present the situation of research and application in algorithm structure.

You will also see the comparison between PSO and Genetic Algorithm (GA). GA is easier than PSO to find the global optimum due to the mutation effect. However, the computation of GA is relatively more complicated than PSO.

PSO can be applied for various optimization problems, for example, Energy-Storage Optimization. PSO can simulate the movement of a particle swarm and can be applied in visual effects like those special effects in the Hollywood film. Could you tell the difference between Particle swarm optimization and Genetic Algorithm now? If not, here is an essay to tell the comparison of three evolutionary Algorithms: GA, PSO, and differential evolution(DE).

This paper talks about the general observations on the similarities and differences among the three algorithms based on computational steps. You can see the three graph Flowcharts, They will give you a simple idea of how these algorithms different from each other.