Abstract
Memetic algorithms (MAs) which mimic culture evolution are population based heuristic searching approaches for the optimization problems. This paper presents a new memetic algorithm called shuffled particle swarm optimization (SPSO), which combines the learning strategy of particle swarm optimization (PSO) and the shuffle strategy of shuffled frog leaping algorithm (SFLA). In the proposed algorithm, the population is partitioned into several memeplexes according to the performance, and the memotypes in each memeplex evolve according to the self-learning and the learning from the best memotype of the memeplex. Furthermore, the memeplexes are shuffled and separated again to continue the evolutionary process. The combination approach contributes to the local exploration and the global exploration of SPSO. Experimental studies on the continuous parametric benchmark problems show the robustness and the global convergence property of the proposed memetic algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI (1975)
Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Caltech Concurrent Computation Program, Tech. Rep., California Institute of Technology, Pasadena, California, USA (1989)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, IEEE Service Center, Piscataway, NJ, pp. 1942-1948 (1995)
Eberhart, R., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a Colony of Cooperating Cgents. IEEE Transactions Systems, Man and Cybernetics 26(1), 29–41 (1996)
Eusuff, M.M., Lansey, K.E.: Water distribution network design using the shuffled frog leaping algorithm. World Water Congress (2001)
Moscato, P.: A Memetic Approach for the Traveling Salesman Problem Implementation of a Computational Ecology for Combinatorial Optimization on Message-Passing Systems. In: Valero, M., Onate, E., Jane, M., Larriba, J.L., Suarez, B. (eds.) Parallel Computing and Transputer Applications, pp. 176–177. IOS Press, Amsterdam, The Netherlands (1992)
Moscato, P., Cotta, C.: A Gentle Introduction to Memetic Algorithms. In: Handbook of Meta-heuristics, pp. 1–56. Kluwer, Dordrecht (1999)
Merz, P.: Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies. University of Siegen, Siegen (2000)
Berretta, R., Rodrigues, L.F.: A Memetic Algorithm for a Multistage Capacitated Lot Sizing Problem. International Journal of Production Economics 87, 67–81 (2004)
Muruganandam, A., Prabhaharan, G., Asokan, P., Baskaran, V.: A Memetic Algorithms Approach to the Cell Formation Problem. International Journal of Advanced Manufacturing Technology 25, 988–997
Kim, S.S., Smith, A.E., Lee, J.H.: A Memetic Algorithm for Channel Assignment in Wireless FDMA Systems. Computers and Operations Research 34(6), 1842–1856 (2007)
Shi, Y.H., Eberhart, R.C.: Parameter Selection in Particle Swarm Optimization. In: The 7th Annual Conference on Evolutionary Programming, San Diego, USA (1998)
Carlisle, A., Dozier, G.: An Off-The-Shelf PSO. In: Proceeding of the 2001 Workshop on Particles Swarm Optimization, Indianapolis, pp. 1–6 (2001)
Eusuff, M.M., Lansey, K.E.: Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm. Journal of Water Resource Planning and Management (2003)
Eusuff, M.M., Lansey, K.E., Pasha, F.: Shuffled Frog-Leaping Algorithm: A Memetic Meta-heuristic for Discrete Optimization. Engineering and Technology, Mathematics and Optimization 38(2), 129–154 (2006)
Duan, Q., Gupta, V.K., Sorooshian, S.: A Shuffled Complex Evolution Approach for Effective and Efficient Global Minimization. Optimization Theory and Application 76(3), 501–521 (1993)
Duan, Q., Sorooshian, S., Gupta, V.K.: Effective and Efficient Global Optimization for Conceptual Rainfall-Runoff Models. Water Resources Research 28(4), 1031–1051 (1992)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhen, Z., Wang, Z., Gu, Z., Liu, Y. (2007). A Novel Memetic Algorithm for Global Optimization Based on PSO and SFLA. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-74581-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74580-8
Online ISBN: 978-3-540-74581-5
eBook Packages: Computer ScienceComputer Science (R0)