A clustering particle swarm optimizer for dynamic optimization.
In the real world, many applications are nonstationary optimization problems. This requires that optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a dynamic environment. To achieve this, this paper proposes a clustering particle swarm optimizer (CPSO) for dynamic optimization problems. The algorithm employs hierarchical clustering method to track multiple peaks based on a nearest neighbor search strategy. A fast local search method is also proposed to find the near optimal solutions in a local promising region in the search space. Six test problems generated from a generalized dynamic benchmark generator (GDBG) are used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for locating and tracking multiple optima in dynamic environments.
Citation : Lli, C. and Yang, S. (2009) A clustering particle swarm optimizer for dynamic optimization. In: Proceedings of the 2009 IEEE Congress on Evoluationary Computation, Trondheim, 2009. New York: IEEE, pp. 439-446.
ISBN : 978-1-4244-2958-5
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes