Distributed adaptive particle swarm optimizer in dynamic environment

Particle Swarm Optimization (PSO) is a population-based stochastic optimization technique, which can be used to find an optimal, or near optimal, solution to a numerical and qualitative problem. In PSO algorithm, the problem solution emerges from the interactions among many simple individual agents called particles. In the real world, we have to frequently deal with searching and tracking an optimal solution in a dynamical and noisy environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the non-stationary solution. The traditional PSO algorithm lacks the ability to track the changing optimal solution in a dynamic and noisy environment. In this paper, we present a distributed adaptive PSO (DAPSO) algorithm that can be used to track a non-stationary optimal solution in a dynamically changing and noisy environment.