Summary
This chapter presents a synergistic combination of particle swarm optimization and evolutionary algorithm for optimization problems. The performance of the hybrid algorithm is bench-marked against conventional genetic algorithm and particle swarm optimization algorithm. Finally, the hybrid algorithm is illustrated as a multiobjective optimization algorithm using the Fonseca 2-objective function.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hondroudakis, A, Malard, J and Wilson, GV, An Introduction to Genetic Algorithms Using RPL2: The EPIC Version, Computer Based Learning Unit, University of Leeds, 1995.
Digalakis, JG, and Margaritis, KG, An Experimental Study of Benchmarking Functions for Genetic Algorithms, 2000 IEEE International Conference on Systems, Man, and Cybernetics, Nashville, vol. 5, pp. 3810–3815, 2000.
Sinclair, MC, The application of a genetic algorithm to trunk network routing table optimization,” 10th.Performance Engineering in Telecommunications Network Teletraffic Symposium, pp. 2/1–2/6, 1993.
Greenwood, GW, Lang, C, and Hurley, S, Scheduling Tasks in Real-time Systems Using Evolutionary Strategies, Proceedings of the Third Workshop on Parallel and Distributed Real-Time Systems, pp. 195–196, 1995.
Fogel, D, and Sebald, AV, Use of Evolutionary Programming in the Design Of Neural Networks For Artifact Detection,” Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1408–1409, 1990.
Meshref, H, and VanLandingham, H, Artificial Immune Systems: Application to Autonomous Agents, 2000 IEEE International Conference on Systems, Man, and Cybernetics, Nashville, Vol. 1, pp. 61–66, 2000.
Di Stefano, C and Tettamanzi, AGB, An Evolutionary Algorithm for Solving the School Time-Tabling Problem, Proceedings of Applications of Evolutionary Computing, pp. 452–462, 2001.
Srinivasan, D, Seow, TH, and Xu, JX, Automated Time Table Generation Using Multiple Context for University Modules, Proceedings of IEEE Congress of Evolutionary Computation, vol. 2, pp. 1751–1756, 2002.
Srinivasan, D, Seow, TH, and Xu, JX, Constraint-Based University Time-Tabling Using Evolutionary Algorithm, Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning, Singapore, vol. 2, pp. 252–256, 2002.
Kennedy, J, Eberhart, RC, and Shi, Y, Swarm Intelligence, San Francisco, Morgan Kaufman Publishers, 2002.
Eberhart, RC, and Shi, Y, Particle Swarm Optimization: Developments, Applications and Resources, Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, vol. 1, pp. 81–86, 2001.
Zhang, C, Shao, H, and Li, Y, Particle Swarm Optimisation for Evolving Artificial Neural Network, 2000 IEEE International Conference on Systems, Man, and Cybernetics, vol. 4, pp. 2487–2490, 2000.
Angeline, PJ, Using Selection to Improve Particle Swarm Optimization, The 1998 IEEE International Conference on IEEE World Congress on Computational Intelligence Evolutionary Computation Proceedings, Alaska, pp. 84–89, 1998.
Lvbjerg, M, Rasmussen, T, and Krink, T, Hybrid Particle Swarm Optimiser with Breeding and Subpopulations, Proceedings of the Genetic and Evolutionary Computation Conference, 2001.
Tan, KC, Lee, TH, and Khor, EF, Evolutionary Algorithms with Goal and Priority Information for Multiobjective Optimzation,” In Proceedings of the Congress in Evolutionary Computation, Washington, DC, vol. 1, pp. 106–113, 1999.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag London Limited
About this chapter
Cite this chapter
Srinivasan, D., Seow, T.H. (2005). Particle Swarm Inspired Evolutionary Algorithm (PS-EA) for Multi-Criteria Optimization Problems. In: Abraham, A., Jain, L., Goldberg, R. (eds) Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-137-7_7
Download citation
DOI: https://doi.org/10.1007/1-84628-137-7_7
Publisher Name: Springer, London
Print ISBN: 978-1-85233-787-2
Online ISBN: 978-1-84628-137-2
eBook Packages: Computer ScienceComputer Science (R0)