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 global best solution in a dynamic environment. To achieve this, this paper proposes a multi-swarm algorithm based on fast particle swarm optimization for dynamic optimization problems. The algorithm employs a mechanism to track multiple peaks by preventing overcrowding at a peak and a fast particle swarm optimization algorithm as a local search method to find the near optimal solutions in a local promising region in the search space. The moving peaks benchmark function is used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for dynamic optimization problems.
Citation : Li, C. and Yang, S. (2008) Fast multi-swarm optimatization for dynamic optimization problems. In: Proceedings of the 4th International Conference on Natural Computation, ICNC '08. Jinan, China, October 2008. Vol. 7. New York: IEEE, pp. 624-628.
ISBN : 9780769533049
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes