Particle Swarm Optimization has been known as PSO. It is a computational method which optimizes a problem by iteratively trying to improve a candidate’s solution with respect to a given measure of quality. In this problem is solved with population of candidate having solutions. Where particles are dubbed and moved. Then these particles are moved here and there in the search-space by simple mathematical formulae which depend upon position & velocity of a particle. Movement of every particle can be influenced with local best known position in search-space best known positions is also arranged. Furthermore updated as good positions and these positions are taken by particles and then expected to relocate swarm to the best solutions available. PSO is also not using gradient of issues that have been optimized.
Particle swarm optimization has been known as popular calculation mechanism. It is optimizing the issues in iterative manner. It has been used to maximize a candidate solution s per the offered quality measure. It has capability to sort out the issues by using sample of candidate population.
Algorithm of PSO
PSO is one of the most famous and very useful met heuristics in the current age hence it showed the success of various optimization problems after applied on. The basic principle of this model is self-organization that describes the dynamics of complex systems. PSO uses an extremely streamlined model of social conduct to take care of the optimization problems, in a cooperative and intelligent framework.
Relationship of reinforcement learning to Swarm Intelligence