Affiliation:
1. Department of Computer Science Faculty of Information Technology Yarmouk University Irbid 21163 JORDAN
Abstract
Parallel bio-inspired algorithms have been successful in solving multi-objective optimisation problems. In this work, we discuss a parallel particle swarm algorithm with added clustering for solving multi-objective optimisation problems. The aim of this work is to perform sensitivity analysis of the parallel particle swarm algorithm. We need to see how the added parallelism improves the overall execution time. Also, looked at the effect of different strategies for population initialisation (such as mutating current set of leaders, random population and lookup in archive for nearest points using geometric calculation). The results show that using different migration frequencies for scattering reduced the overall overlap between processors. Results regarding how clustering and gathering affect performance metrics are also reported.
Publisher
World Scientific and Engineering Academy and Society (WSEAS)
Reference21 articles.
1. Carlos A. Coello, David A. Van Veldhuizen, and Gary B. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York, May 2002. ISBN 0-3064-6762-3.
2. J. Kennedy and R. Eberhart. Swarm Intelligence. Morgan Kaufmann Publishers, 2001.
3. J. Kennedy and R. Eberhart. Particle swarm optimization. In Proc. of the IEEE Int. Conf. on Neural Networks, pages 1942–1948, Piscataway, NJ, USA, 1995.
4. Abdallah Qteish, Mohammad Hamdan. Hybrid particle swarm and conjugate gradient optimization algorithm. International Conference in Swarm Intelligence pages 582–588. Springer, 2010.
5. Y. Shi and R. Eberhart. A modified particle swarm optimizer. In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 67–73, 1998.