| The Particle Swarm Optimizer is modified to create the Adaptive Particle Swarm Optimizer. This variation is better able to locate and track optima in dynamic environments. The modification is shown to marginally increase costs while the environment is stationary, but to be efficient and effective when the environment is dynamic. The Adaptive Particle Swarm Optimizer recognizes changes in the search space by means of one or more particles designated as sentries, and adjusts to these changes in the environment by reevaluating and possibly replacing individual particle memories. The effectiveness of the modification is demonstrated by application to a variety of computational and combinatoric problems, including constraint satisfaction and constrained optimization problems. |
