Abstract
In many real world optimization problems, several optimization goals have to be considered in parallel. For this reason, there has been a growing interest in multi-objective optimization (MOO) in the past many years. Several new approaches have recently been proposed, which produced very good results. However, existing techniques have solved mainly problems of “low dimension”, i.e., with less than 10 optimization objectives. This chapter proposes a new computational algorithm whose design is inspired by particle mechanics in physics. The algorithm is capable of solving MOO problems of high dimensions. There is a deep and useful connection between particle mechanics and high dimensional MOO. This connection exposes new information and provides an unfamiliar perspective on traditional optimization problems and approaches. The alternative of particle mechanics algorithm (PMA) to traditional approaches can deal with a variety of complicated, large scale, high dimensional MOO problems.
This is a preview of subscription content, log in via an institution to check access.
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
Ulungu, E.L., Teghem, J.: The two phases method: an efficient procedure to solve bi-objective combinatorial optimization problems. Journal Foundations of Computing & Decision Sciences 20(2), 149–165 (1995)
Tuyttens, D., Teghem, J., Fortemps, P., Van Nieuwenhuyse, K.: Performance of the MOSA method for the bicriteria assignment problem. Journal of Heuristics 6, 295–310 (2000)
Pirlot, M.: General local search methods. Europe Journal of Operational Research 92, 493–511 (1996)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Forrest, S.: Genetic algorithms—principles of natural-selection applied to computation. Science 261(5123), 872–878 (1993)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Inspiration for optimization from social insect behaviour. Nature 406(6791), 39–42 (2000)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE Conf. Neural Networks, Piscataway, NJ, vol. IV, 1942–1948 (1995)
Durbin, R., Willshaw, D.: An analogue approach to the travelling salesman problem using an elastic net method. Nature 326(6114), 689–691 (1987)
Shuai, D., Feng, X.: Distributed Problem Solving in Multi-Agent Systems: A Spring Net Approach. IEEE Intelligent Systems 20(4), 66–74 (2005)
Shuai, D., Feng, X.: The Parallel Optimization of Network Bandwidth Allocation Based on Generalized Particle Model. Computer Networks 50(9), 1219–1246 (2006)
Le Boudec, J.-Y.: Rate adaptation, Congestion Control and Fairness: A Tutorial (November 22, 2005)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: Afast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Feng, X., Lau, F.C.M. (2009). Nature-Inspired Particle Mechanics Algorithm for Multi-Objective Optimization. In: Goh, CK., Ong, YS., Tan, K.C. (eds) Multi-Objective Memetic Algorithms. Studies in Computational Intelligence, vol 171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88051-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-88051-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88050-9
Online ISBN: 978-3-540-88051-6
eBook Packages: EngineeringEngineering (R0)