Abstract
This paper presents a novel nature-inspired multi-objective optimization algorithm. The method extends the glowworm swarm particles optimization algorithm with algorithmical enhancements which allow to identify optimal pareto front in the objectives space. In addition, the system allows to specify constraining functions which are needed in practical applications. The framework has been applied to the power dispatch problem of distribution systems including Distributed Energy Resources (DER). Results for the test cases are reported and discussed elucidating both numerical and complexity analysis.
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
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6, 182–197 (2000)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In: Giannakoglou, K.C., et al. (eds.) Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pp. 95–100 (2002)
Ehrgott, M.: Multicriteria Optimization. Lecture Notes in Economics and Mathematical Systems. Springer (2005)
Coello, C.A.C., Lamont, G.B., Van Veldhuisen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic and Evolutionary Computation Series. Springer (2007)
Hwang, C.L., Masud, A.S.M.: Multiple objective decision making, methods and applications: a state-of-the-art survey. Lecture Notes in Economics and Mathematical Systems. Springer (1979)
Charnes, A., Cooper, W.W.: Management models and industrial applications of linear programming. In: Management Models and Industrial Applications of Linear Programming, vol. 2, Wiley (1961)
Krishnanand, K.N., Ghose, D.: Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and Grid Systems 2(3), 209–222 (2006)
Riva Sanseverino, E., Di Silvestre, M.L., Gallea, R.: Multi-modal search for multiobjective optimization: an application to optimal smart grids management. In: MedPower. IET (2012)
Padhy, N.P.: Unit commitment-a bibliographical survey. IEEE Transactions on Power Systems 19(2), 1196–1205 (2004)
Vahedi, H., Noroozian, R., Hosseini, S.H.: Optimal management of microgrid using differential evolution approach. In: 2010 7th International Conference on the European Energy Market (EEM), pp. 1–6 (June 2010)
Graditi, G., Ippolito, M.G., Riva Sanseverino, E., Zizzo, G.: Optimal set points regulation of distributed generation units in micro-grids under islanded operation. In: 2010 IEEE International Symposium on Industrial Electronics (ISIE), pp. 2253–2260 (July 2010)
Zhang, H., Fu, P., Liu, Y.: Parameter settings analysis for glowworm swarm optimization algorithm. Journal of Information and Computational Science 9(11), 3231–3240 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Riva Sanseverino, E., Di Silvestre, M.L., Gallea, R. (2013). Pareto-optimal Glowworm Swarms Optimization for Smart Grids Management. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-37192-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37191-2
Online ISBN: 978-3-642-37192-9
eBook Packages: Computer ScienceComputer Science (R0)