nach oben

Cognitive Computation

Erschienen in:

01.10.2015

A Biologically Inspired Modified Flower Pollination Algorithm for Solving Economic Dispatch Problems in Modern Power Systems

verfasst von: Hari Mohan Dubey, Manjaree Pandit, B. K. Panigrahi

Erschienen in: Cognitive Computation | Ausgabe 5/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Gradient-based traditional algorithms fail to locate optimal solutions for real-world problems with non-differentiable/discontinuous objective functions. But biologically inspired optimization algorithms, due to their unconventional random search capability, provide good solutions within finite time to multimodal and non-convex problems. The search capability of these methods largely depends on their exploration and exploitation potential. This paper presents a modified flower pollination algorithm (MFPA) in which (1) the local pollination of FPA is controlled by a scaling factor and (2) an intensive exploitation phase is added to tune the best solution. The effectiveness of MFPA is tested on some mathematical benchmarks and four large practical power system test cases.

Vorheriger Artikel An Improved SVM-Based Cognitive Diagnosis Algorithm for Operation States of Distribution Grid

Nächster Artikel What Goes Around Comes Around: Learning Sentiments in Online Medical Forums

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Wood AJ, Wollenberg BF. Power generation operation and control. 2nd ed. NewYork: Wiley; 1996.

Sinha N, Chakrabarti R, Chattopadhyay PK. Evolutionary programming techniques for economic load dispatch. IEEE Trans Evol Comput. 2003;7(1):83–94.CrossRef

Selvakumar A, Thanushkodi K. A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans Power Syst. 2007;22(1):42–51.CrossRef

Chaturvedi KT, Pandit M, Srivastava L. Self-organizing hierarchical particle swarm optimization for nonconvex economic dispatch. IEEE Trans Power Syst. 2008;23(3):1079–87.CrossRef

Vlachogiannis JG, Lee KY. Economic load dispatch a comparative study on heuristic techniques with an improved coordinated aggregation based PSO. IEEE Trans Power Syst. 2009;24(2):991–1001.CrossRef

Niknam T, Doagou Mojarrad H, Nayeripour M. A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch. Energy. 2010;35(4):1764–78.CrossRef

Noman N, Iba H. Differential evolution for economic load dispatch problems. Electr Power Syst Res. 2008;78(8):1322–31.CrossRef

Chiou JP. Variable scaling hybrid differential evolution for large-scale economic dispatch problems. Electr Power Syst Res. 2007;77(3–4):212–8.CrossRef

Panigrahi BK, Pandi VR. Bacterial foraging optimisation: Nelder Mead hybrid algorithm for economic load dispatch. IET Proc Gener Transm Distrib. 2008;2(4):556–65.CrossRef

10.

Bhattacharya A, Chattopadhyay PK. Biogeography-based optimization for different economic load dispatch problems. IEEE Trans Power Syst. 2010;25(2):1064–77.CrossRef

11.

Pandi VR, Panigrahi BK, Bansal RC, Das S, Mohapatra A. Economic load dispatch using hybrid swarm intelligence based harmony search algorithm. Electr Power Compon Syst. 2011;39(8):751–67.CrossRef

12.

Dalvand M, Ivatloo B, Najafi A, Rabiee A. Continuous quick group search optimizer for solving non-convex economic dispatch problems. Electr Power Syst Res. 2012;93:93–105.CrossRef

13.

Zare K, Haque M, Davoodi E. Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method. Electr Power Syst Res. 2012;84(1):83–9.CrossRef

14.

Yang X, Hosseini S, Gandomi A. Firefly algorithm for solving non-convex economic dispatch with valve point loading effect. Appl Soft Comput. 2012;12(3):1180–6.CrossRef

15.

Lohokare M, Panigrahi BK, Pattnaik S, Devi S, Mohapatra A. Neighborhood search-driven accelerated biogeography-based optimization for optimal load dispatch. IEEE Trans Syst Man Cybern Part C. 2012;42(5):641–52.CrossRef

16.

Wang L, Li LP. An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems. Electr Power Energy Syst. 2013;44(1):832–43.CrossRef

17.

Selvakumar A, Thanushkodi K. Optimization using civilized swarm: solution to economic dispatch with multiple minima. Electr Power Syst Res. 2009;79(1):8–16.CrossRef

18.

Selvakumar A, Thanushkodi K. Anti-predatory particle swarm optimization: solution to nonconvex economic dispatch problems. Electr Power Syst Res. 2008;78(1):2–10.CrossRef

19.

Park J, Jeong Y, Shin J, Lee K. An improved particle swarm optimization for nonconvex economic dispatch problems. IEEE Trans Power Syst. 2010;25(1):156–66.CrossRef

20.

Hosseinnezhad V, Babaei E. Economic load dispatch using θ-PSO. Electr Power Energy Syst. 2013;49:160–9.CrossRef

21.

Vishwakarma KK, Dubey HM. Simulated annealing based optimization for solving large scale economic load dispatch problems. International Journal Engineering Research and Technology (IJERT). 2012;1(3):1–8.

22.

Pothiya S, Ngamroo I, Kongprawechnon W. Ant colony optimization for economic dispatch problem with non-smooth cost functions. Int J Electr Power Energy Syst. 2010;32(5):478–87.CrossRef

23.

Coelho L, Souza R, Mariani V. Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems. Math Comput Simul. 2009;79(10):3136–47.CrossRef

24.

Srinivasa Reddy A, Vaisakh K. Shuffled differential evolution for economic dispatch with valve point loading effects. Int J Electr Power Energy Syst. 2013;46:342–52.CrossRef

25.

Hemamalini S, Simon SP. Artificial bee colony algorithm for economic load dispatch problem with non-smooth cost functions. Electr Power Compon Syst. 2010;38(7):786–803.CrossRef

26.

Basu M, Chowdhury A. Cuckoo search algorithm for economic dispatch. Energy. 2013;60:99–108.CrossRef

27.

Bhattacharjee K, Bhattacharya A, Halder nee Dey S. Oppositional real coded chemical reaction optimization for different economic dispatch problems. Int J Electr Power Energy Syst. 2014;55:378–91.CrossRef

28.

Sailesh Babu G, Bhagwan Das D, Patvardhan C. Real-parameter quantum evolutionary algorithm for economic load dispatch. IET Proc Gener Transm Distrib. 2008;2(1):22–31.CrossRef

29.

Sayah S, Hamouda A. A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems. Appl Soft Comput. 2013;13(4):1608–19.CrossRef

30.

Pandit M, Srivastava L, Sharma M, Dubey HM, Panigrahi BK. Large scale multi-zone optimal power dispatch using hybrid hierarchical evolution technique. J Eng. IET digital library, doi: 10.1049/joe.2013.0262.

31.

Bhattacharya A, Chattopadhyay PK. Hybrid differential evolution with biogeography-based optimization for solution of economic load dispatch. IEEE Trans Power Syst. 2010;25(4):1955–64.CrossRef

32.

Dubey HM, Pandit M, Panigrahi BK, Udgir M. Economic load dispatch by hybrid swarm intelligence based gravitational search algorithm. Int J Intell Syst Appl. 2013;5(8):21–32.

33.

Niu Q, Zhang H, Wang X, Li K, Irwin GW. A hybrid harmony search with arithmetic crossover operation for economic dispatch. Int J Electr Power Energy Syst. 2014;62:237–57.CrossRef

34.

Coelho LDS, Bora TC, Mariani VC. Differential evolution based on truncated Lévy-type flights and population diversity measure to solve economic load dispatch problems. Int J Electr Power Energy Syst. 2014;57:178–88.CrossRef

35.

Hosseinnezhad V, Rafiee M, Ahmadian M, Taghi Ameli MT. Species-based quantum particle swarm optimization for economic load dispatch. Int J Electr Power Energy Syst. 2014;63:311–22.CrossRef

36.

Nazemi A, Nazemi M. A gradient-based neural network method for solving strictly convex quadratic programming problems. Cogn Comput. 2014;6(3):484–95.CrossRef

37.

Tang Q, Shen Y, Hu C, Zeng J, Gong W. Swarm intelligence: based cooperation optimization of multi-modal functions. Cogn Comput. 2013;5(1):48–55.CrossRef

38.

Boaro M, Fuselli D, De Angelis F, Liu D. Adaptive dynamic programming algorithm for renewable energy scheduling and battery management. Cogn Comp. 2013;5(2):264–77.CrossRef

39.

Yang XS. Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation 2012. Lecture notes in computer science. Vol. 7445, 2012. p 240–49.

40.

Pavlyukevich I. Lévy flight, non local search and simulated annealing. J Comput Phys. 2007;226(2):1830–44.CrossRef

41.

Yang XS, Karamanoglu M, He X. Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim. 2013;46(9):1222–37.CrossRef

42.

Gandomi AH, Yang XS, Alavi AH. Cuckoo search algorithm: a meta-heuristic approach to solve structural optimization problems. Eng Comput. 2013;29(1):17–35.CrossRef

43.

Orero S, Irving M. Large scale unit commitment using a hybrid genetic algorithm. Int J Electr Power Energy Syst. 1997;19(1):45–55.CrossRef

44.

Rashedi E, Nezamabadi-pour H, Saryazdi S. GSA: a gravitational search algorithm. Inf Sci. 2009;179(13):2232–48.CrossRef

Titel: A Biologically Inspired Modified Flower Pollination Algorithm for Solving Economic Dispatch Problems in Modern Power Systems
verfasst von: Hari Mohan Dubey
Manjaree Pandit
B. K. Panigrahi
Publikationsdatum: 01.10.2015
Verlag: Springer US
Erschienen in: Cognitive Computation / Ausgabe 5/2015
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-015-9324-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 5/2015

What Goes Around Comes Around: Learning Sentiments in Online Medical Forums

An Improved SVM-Based Cognitive Diagnosis Algorithm for Operation States of Distribution Grid

Affordance Estimation Enhances Artificial Visual Attention: Evidence from a Change-Blindness Study

A New Action Execution Module for the Learning Intelligent Distribution Agent (LIDA): The Sensory Motor System

Erratum to: Image Fusion by Hierarchical Joint Sparse Representation

Autonomous Driver Based on an Intelligent System of Decision-Making