Abstract-In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing optima over dynamic environments. To address this requirement, this paper investigates a clustering particle swarm optimizer (PSO) for dynamic optimization problems. This algorithm employs a hierarchical clustering method to locate and track multiple peaks. A fast local search method is also introduced to search optimal solutions in a promising subregion found by the clustering method. Experimental study is conducted based on the moving peaks benchmark to test the performance of the clustering PSO in comparison with several state-of-the-art algorithms from the literature. The experimental results show the efficiency of the clustering PSO for locating and tracking multiple optima in dynamic environments in comparison with other particle swarm optimization models based on the multiswarm method.
A popular example is the clustering PSO (CPSO) REF , where a hierarchical clustering method is used to create multiple swarms by clustering a random swarm whenever a change is detected.
A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Computer Science Mathematics