Now showing items 1-3 of 3
Fast multi-swarm optimatization for dynamic optimization problems.
In the real world, many applications are non-stationary optimization problems. This requires that the optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing ...
Multi-population methods in unconstrained continuous dynamic environments: the challenges
The multi-population method has been widely used to solve unconstrained continuous dynamic optimization problems with the aim of maintaining multiple populations on different peaks to locate and track multiple changing ...
An adaptive learning particle swarm optimizer for function optimization.
Traditional particle swarm optimization (PSO) suffers from the premature convergence problem, which usually results in PSO being trapped in local optima. This paper presents an adaptive learning PSO (ALPSO) based on a ...