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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  1/2018 - 15

Optimization of Charge/Discharge Coordination to Satisfy Network Requirements Using Heuristic Algorithms in Vehicle-to-Grid Concept

DOGAN, A. See more information about DOGAN, A. on SCOPUS See more information about DOGAN, A. on IEEExplore See more information about DOGAN, A. on Web of Science, BAHCECI, S. See more information about  BAHCECI, S. on SCOPUS See more information about  BAHCECI, S. on SCOPUS See more information about BAHCECI, S. on Web of Science, DALDABAN, F. See more information about  DALDABAN, F. on SCOPUS See more information about  DALDABAN, F. on SCOPUS See more information about DALDABAN, F. on Web of Science, ALCI, M. See more information about ALCI, M. on SCOPUS See more information about ALCI, M. on SCOPUS See more information about ALCI, M. on Web of Science
 
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Download PDF pdficon (1,247 KB) | Citation | Downloads: 238 | Views: 778

Author keywords
electric vehicles, genetic algorithms, heuristic algorithms, smart grids, optimization

References keywords
grid(33), power(31), electric(28), vehicle(23), vehicles(21), energy(21), charging(18), plug(16), systems(14), smart(14)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 121 - 130
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01015
Web of Science Accession Number: 000426449500015
SCOPUS ID: 85043247244

Abstract
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Full text preview
Image thresholding is the most crucial step in microscopic image analysis to distinguish bacilli objects causing of tuberculosis disease. Therefore, several bi-level thresholding algorithms are widely used to increase the bacilli segmentation accuracy. However, bi-level microscopic image thresholding problem has not been solved using optimization algorithms. This paper introduces a novel approach for the segmentation problem using heuristic algorithms and presents visual and quantitative comparisons of heuristic and state-of-art thresholding algorithms. In this study, well-known heuristic algorithms such as Firefly Algorithm, Particle Swarm Optimization, Cuckoo Search, Flower Pollination are used to solve bi-level microscopic image thresholding problem, and the results are compared with the state-of-art thresholding algorithms such as K-Means, Fuzzy C-Means, Fast Marching. Kapur's entropy is chosen as the entropy measure to be maximized. Experiments are performed to make comparisons in terms of evaluation metrics and execution time. The quantitative results are calculated based on ground truth segmentation. According to the visual results, heuristic algorithms have better performance and the quantitative results are in accord with the visual results. Furthermore, experimental time comparisons show the superiority and effectiveness of the heuristic algorithms over traditional thresholding algorithms.


References | Cited By  «-- Click to see who has cited this paper

[1] U.S. Energy Administration Office, International Energy Outlook, Washington, DC, USA, DOE/EIA-0484(2016), May 2016. [Online] Available: Temporary on-line reference link removed - see the PDF document

[2] Electric Power Research Institute, Environmental assessment of plug-in hybrid electric vehicles. Volume 1: Nationwide Greenhouse Gas Emissions, CA, USA, 1015325, July 2007. [Online] Available: Temporary on-line reference link removed - see the PDF document

[3] International Energy Agency. Global EV Outlook 2017 Two million and counting. Paris, FR, OECD/IEA 2017, July 2017. [Online] Available: Temporary on-line reference link removed - see the PDF document

[4] X. Fang, S. Misra, G. Xue, and D. Yang, "Smart Grid – The New and Improved Power Grid: A Survey ," IEEE Commun. Surveys Tuts., vol. 14, no. 4, pp. 944–980, 2012.
[CrossRef] [Web of Science Times Cited 910] [SCOPUS Times Cited 1205]


[5] S. Xie, W. Zhong, K. Xie, R. Yu, and Y. Zhang, "Fair Energy Scheduling for Vehicle-to-Grid Networks Using Adaptive Dynamic Programming," IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 8, pp. 1697–1707, 2016.
[CrossRef] [Web of Science Times Cited 41] [SCOPUS Times Cited 42]


[6] E. De Caluwé, Grid-supportive charging infrastructure for plug-in electric vehicles, PhD thesis, K.U.Leuven, – Faculty of Engineering Science, 2015. [Online] Available: Temporary on-line reference link removed - see the PDF document

[7] J. Taylor, A. Maitra, M. Alexander, D. Brooks, and M. Duvall, "Evaluation of the impact of plug-in electric vehicle loading on distribution system operations," in Proc. IEEE Power Energy Soc. Gen. Meet., Calgary, Canada, 26-30 July 2009 pp. 1–6.
[CrossRef] [SCOPUS Times Cited 297]


[8] M. J. Scott, M. K. Meyers, D. B. Elliott, W. M. Warwick, "Impacts Assessment of Plug-in Hybrid Vehicles on Electric Utilities and Regional US Power Grids Part 2: Economic Assessment," Pacific Northwest Nat. Lab., Richland, WA., DE-AC05-76RL01830, Nov. 2017 [Online] Available: Temporary on-line reference link removed - see the PDF document

[9] A. Dogan, M. Kuzlu, M. Pipattanasomporn, S. Rahman, and T. Yalcinoz, "Impact of EV charging strategies on peak demand reduction and load factor improvement," in Proc. Inter. Conf. on Elect.l and Electronics Eng., Bursa, Turkey, 26-28 Nov. 2015, pp. 374-378.
[CrossRef] [SCOPUS Times Cited 5]


[10] C. Guille and G. Gross, "A conceptual framework for the vehicle-to-grid ( V2G ) implementation," Energy Policy, vol. 37, no. 11, pp. 4379–4390, 2009.
[CrossRef] [Web of Science Times Cited 395] [SCOPUS Times Cited 555]


[11] M. Yilmaz and P. T. Krein, "Review of the impact of vehicle-to-grid technologies on distribution systems and utility interfaces," IEEE Trans. Power Electron., vol. 28, no. 12, pp. 5673–5689, Dec. 2013.
[CrossRef] [Web of Science Times Cited 304] [SCOPUS Times Cited 357]


[12] C. S. Antunez, J. F. Franco, M. J. Rider, R. Romero, "A New Methodology for the Optimal Charging Coordination of Electric Vehicles Considering Vehicle-to-Grid Technology," IEEE Trans. Sustain. Energy. vol. 7, no. 2, pp. 596–607, 2016.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 14]


[13] K. Clement-nyns, E. Haesen, and J. Driesen, "The impact of vehicle-to-grid on the distribution grid," Electr. Power Syst. Res., vol. 81, no. 1, pp. 185–192, 2011.
[CrossRef] [Web of Science Times Cited 170] [SCOPUS Times Cited 225]


[14] H. Liu, Z. Hu, Y. Song, and J. Lin, "Decentralized vehicle-to-grid control for primary frequency regulation considering charging demands," IEEE Trans. Power Syst., vol. 28, no. 3, pp. 3480–3489, Aug. 2013.
[CrossRef] [Web of Science Times Cited 160] [SCOPUS Times Cited 191]


[15] C. D. White and K. M. Zhang, "Using vehicle-to-grid technology for frequency regulation and peak-load reduction," J. Power Sources, vol. 196, no. 8, pp. 3972–3980, 2011.
[CrossRef] [Web of Science Times Cited 119] [SCOPUS Times Cited 163]


[16] Z. Wang and S. Wang, "Grid Power Peak Shaving and Valley Filling Using Vehicle-to-Grid Systems," IEEE Trans. Power Del., vol. 28, no. 3, pp. 1822–1829, 2013.
[CrossRef] [Web of Science Times Cited 107] [SCOPUS Times Cited 131]


[17] M. Brenna, F. Foiadelli, and M. Longo, "The Exploitation of Vehicle-to-Grid Function for Power Quality Improvement in a Smart Grid," IEEE Intell. Transp. Syst. vol. 15, no. 5, pp. 2169–2177, 2014.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 26]


[18] H. Liu, Z. Hu, Y. Song, J. Wang, and X. Xie, "Vehicle-to-Grid Control for Supplementary Frequency Regulation Considering Charging Demands," IEEE Trans. Power Syst., vol. 30, no. 6, pp. 3110–3119, 2015.
[CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 80]


[19] M. Kesler, M. C. Kisacikoglu, and L. M. Tolbert, "Vehicle-to-Grid Reactive Power Operation Using Plug-In Electric Vehicle Bidirectional Offboard Charger," IEEE Ind. Electron., vol. 61, no. 12, pp. 6778–6784, 2014.
[CrossRef] [Web of Science Times Cited 81] [SCOPUS Times Cited 103]


[20] J. Lin, S. Member, K. Leung, V. O. K. Li, and A. In, "Optimal Scheduling With Vehicle-to-Grid Regulation Service," IEEE Internet Things J., vol. 1, no. 6, pp. 556–569, 2014.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 46]


[21] X. Wu, L. Li, J. Zou, and G. Zhang, "EV-Based Voltage Regulation in Line Distribution Grid." IEEE Instr. and Meas. Tech. Conf. Taipei 2016.
[CrossRef] [SCOPUS Times Cited 4]


[22] A. Andreotti, G. Carpinelli, F. Mottola, and D. Proto, "A review of single-objective optimization models for plug-in vehicles operation in smart grid- Part I: Theoretical aspects," in Proc. Power and Energy Society General Meeting, San Diego, USA, 22-26 July 2012, pp. 1-8
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 23]


[23] A. Andreotti, G. Carpinelli, F. Mottola, and D. Proto "A review of single-objective optimization models for plug-in vehicles operation in smart grids part ii: Numerical applications to vehicles fleets," in Proc. Power and Energy Society General Meeting, San Diego, USA, 22-26 July 2012, pp. 1-8.
[CrossRef] [SCOPUS Times Cited 20]


[24] X. Bai, W. Qiao, "Robust optimization for bidirectional dispatch coordination of large-scale V2G," IEEE Trans Smart Grid, vol. 6, no. 4, pp. 1944–1954, 2015.
[CrossRef] [Web of Science Times Cited 35] [SCOPUS Times Cited 38]


[25] W. Qi, Z. Xu , Z-J. Shen, Hu Z, Song Y. "Hierarchical coordinated control of plug- in electric vehicles charging in multifamily dwellings,"IEEE Trans Smart Grid, vol. 5, no. 3, pp. 1465–1474, 2014.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 56]


[26] C. Jin, , J. Tang, and P. Ghosh, "Optimizing Electric Vehicle Charging: A Customer’s Perspective," IEEE Trans. Veh. Technol., vol. 62, no. 7, pp. 2919–2927, 2013.
[CrossRef] [Web of Science Times Cited 93] [SCOPUS Times Cited 107]


[27] A. H. Hajimiragha, C. A. Canizares, M. W. Fowler, S. Moazeni, and A. Elkamel, "A robust optimization approach for planning the transition to plug-in hybrid electric vehicles," IEEE Trans. Power Syst, vol. 26, no. 4, pp. 2264–2274, 2011.
[CrossRef] [Web of Science Times Cited 85] [SCOPUS Times Cited 101]


[28] K. Zhang, L. Xu, M. Ouyang, H. Wang, L. Lu, J. Li, "Optimal decentralized valley-filling charging strategy for electric vehicles,"Energy Convers Manag., vol. 78, no. 57, pp. 537–550, 2009.
[CrossRef] [Web of Science Times Cited 51] [SCOPUS Times Cited 53]


[29] X. Wang, Q. Liang, "Energy management strategy for plug-in hybrid electric vehicles via bidirectional vehicle-to-grid," IEEE Syst J, vol. 37, no. 3, pp. 1789 - 1798, 2017.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 22]


[30] M. Shafie-khah, M. P. Moghaddam, M. K. Sheikh-El-Eslami, M. Rahmani- Andebili, "Modeling of interactions between market regulations and behavior of plug-in electric vehicle aggregators in a virtual power market environment," Energy, vol. 40, no. 1, pp. 139-150, 2012.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 49]


[31] Z. Yang, K. Li , A. Foley, C. Zhang, "Optimal Scheduling Methods to Integrate Plug-in Electric Vehicles with the Power System: A Review," in Proc. 19th IFAC World Congress, Cape Town, South Africa, 24-29 August 2014.
[CrossRef]


[32] Z. Yang, K. Li , A. Foley, "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, vol. 51, no. 28, pp. 396-416, 2015.
[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 59]


[33] Y. Sugii, K. Tsujino, T. Nagano, "A Genetic-Algorithm based scheduling method of charging of electric vehicles," in Proc. IEEE Systems, Man, and Cybernetics, Conference Proceedings, Tokyo, Japan, 12-15 Oct. 1999, pp. 1-8.
[CrossRef]


[34] G. Celli, E. Ghiani, F. Pilo, G. Pisano, G. G. Soma, "Particle Swarm Optimization for Minimizing the Burden of Electric Vehicles in Active Distribution Networks," in Proc. Power and Energy Society General Meeting, in Proc. Power and Energy Society General Meeting, San Diego, USA, 22-26 July 2012, pp. 1-7.
[CrossRef] [SCOPUS Times Cited 17]


[35] S. Xu, D. Feng, Z. Yan, L. Zhang, N. Li, L. Jing, J. Wang, "Ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles," Int. J. Dist. Sensor Network, vol. 9, no. 5, pp. 1–13, 2013.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 25]


[36] I. Rahman, P. Vasant, B. S. M. Singh, M. Abdullah-Al-WadudHybrid, "Swarm Intelligence-Based Optimization for Charging Plug-in Hybrid Electric Vehicle," In: Nguyen N., Trawinski B., Kosala R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science, vol 9012. Springer, Cham
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]


[37] M. Alonso, H. Amaris, J. G. Germain, J. M. Galan, Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms," Energies, vol. 7, no. 4, pp. 2449-2475, 2014.
[CrossRef] [Web of Science Times Cited 53] [SCOPUS Times Cited 63]


[38] C. Jin, J. Tang, P. Ghosh, "Optimizing electric vehicle charging with energy storage in the electricity market," IEEE Trans Smart Grid, vol. 4, no. 1, pp. 311-320, 2013.
[CrossRef] [Web of Science Times Cited 98] [SCOPUS Times Cited 107]


[39] S. Shao, M. Pipattanasomporn, and S. Rahman, "Challenges of PHEV penetration to the residential distribution network," in Proc. IEEE Power Energy Soc. Gen. Meeting, 2009, Calgary, Canada, 26-30 July 2009, pp. 1–8.
[CrossRef] [SCOPUS Times Cited 256]


[40] C. D. White and K. M. Zhang, "Using vehicle-to-grid technology for frequency regulation and peak-load reduction," J. Power Sources, vol. 196, no. 8, pp. 3972-3980, 2011.
[CrossRef] [Web of Science Times Cited 119] [SCOPUS Times Cited 163]


[41] P. Richardson, D. Flynn, A. Keane, "Optimal Charging of Electric Vehicles in Low-Voltage Distribution Systems," IEEE Trans. Power Syst., vol. 27, no. 1, pp. 268 - 279, 2012.
[CrossRef] [Web of Science Times Cited 238] [SCOPUS Times Cited 316]


[42] S. Deilami, A. S. Masoum, P. S. Moses, M. A. S. Masoum, "Real-Time Coordination of Plug-In Electric Vehicle Charging in Smart Grids to Minimize Power Losses and Improve Voltage Profile," IEEE Trans. Smart Grid, vol. 2, no. 3, pp. 456 - 467, 2011.
[CrossRef] [Web of Science Times Cited 459] [SCOPUS Times Cited 598]


[43] N. Banol A., J. F. Franco, M. Lavorato, M. J. Rider, R. Romero, "Plug-In Electric Vehicle Charging Coordination in Electrical Distribution Systems Using a Tabu Search Algorithm," IEEE 15th Int. Conf. Environment and Electrical Engineering (EEEIC), Rome, Italy, 10-13 June 2015, pp. 1-6.
[CrossRef] [SCOPUS Times Cited 2]


[44] O. Sundstrom, C. Binding, "Flexible Charging Optimization for Electric Vehicles Considering Distribution Grid Constraints," IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 26 - 37, 2011.
[CrossRef] [Web of Science Times Cited 235] [SCOPUS Times Cited 300]


[45] A. Dogan, T. Yalcinoz, M. Alci, "A Comparison of Heuristic Methods for Optimum Power Flow Considering Valve Point Effect", Elektronika Ir Elektrotechnika, vol. 22, no.5, pp.32-37, 2016.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 8]


[46] P. Richardson, D. Flynn, and A. Keane, "Optimal charging of electric vehicles in low-voltage distribution systems," IEEE Trans. Power Syst., vol. 27, no. 1, pp. 268–279, 2012.
[CrossRef] [Web of Science Times Cited 238] [SCOPUS Times Cited 316]


[47] C. Wu and H. Mohsenian-rad, "Vehicle-to-Aggregator Interaction Game," IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 434–442, 2012.
[CrossRef] [Web of Science Times Cited 200] [SCOPUS Times Cited 221]


[48] H. Liang, B. J. Choi, and W. Zhuang, "Optimizing the Energy Delivery via V2G Systems Based on Stochastic Inventory Theory," IEEE Trans. Smart Grid., vol. 4, no. 4, pp. 2230–2243, 2013.
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 38]


[49] R.-E. Precup, S. Preitl, "Optimisation criteria in development of fuzzy controllers with dynamics," Engineering Applications of Artificial Intelligence, vol. 17, no. 6, pp. 661-674, 2004.
[CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 53]


[50] T. S. Li, C. T. Su, T. L.Chiang, "Applying robust multi-response quality engineering for parameter selection using a novel neural–genetic algorithm," Computers in Industry, vol. 50, no. 1, pp. 113-122, 2003.
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 39]


[51] S. Vrkalovic, T.-A. Teban, I.-D. Borlea, "Stable Takagi-Sugeno fuzzy control designed by optimization," International Journal of Artificial Intelligence, vol. 15, no. 2, pp. 17-29, 2017.

[52] R. D. Baruah, P. Angelov, "DEC: Dynamically Evolving Clustering and its application to structure identification of evolving fuzzy models," IEEE Trans. Cybern., vol. 44, no. 9, pp. 1619-1631, 2014.
[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 26]


[53] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading: Addison-Wesley Publishing Company, p.62, 1989

[54] K. V. Price, "Differential evolution: a fast and simple numerical optimizer," in Proc. Fuzzy Inf. Process. Soc. Conf. North Am., Berkeley, CA, USA, 19-22 June 1996 pp. 524–527.
[CrossRef]


[55] J. Kennedy, R. Eberhart, "Particle swarm optimization", in Proc. IEEE Int. Conf Neural Networks, Perth, Austuralia, 27 Nov.-1 Dec. 1995, pp. 1942–1948.
[CrossRef] [Web of Science Times Cited 23084]


[56] D. Karaboga, "An idea based on honey bee swarm for numerical optimization". Technical Report TR06, Erciyes University, Eng. Faculty, Computer Engineering Department, Oct. 2005. [Online] Available: Temporary on-line reference link removed - see the PDF document

[57] R. Ranjan, D Das, "Simple and efficient computer algorithm to solve radial distribution networks," Electr Power Compon Syst., vol. 31 pp.95–107, 2003
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 56]


[58] GridLAB-D, [Online]. Available: http://www.gridlabd.org/

[59] D. P. Chassin, K. Schneider, C. Gerkensmeyer, "GridLABD: An open-source power systems modeling and simulation environment," in Proc. Transmission and Distribution Conference and Exposition, Chicago, USA, 21-24 April 2008, pp.1-5.
[CrossRef] [SCOPUS Times Cited 173]


[60] MATLAB, [Online]. Available: https://www.mathworks.com/products/matlab.html

[61] J. C. Fuller, B. Vyakaranam, N. Prakash Kumar, S. M. Leistritz, G. B. Parker, "Modeling of GE Appliances in GridLAB-D: Peak Demand Reduction," Technical Report-PNNL-21358 [Online] Available: Temporary on-line reference link removed - see the PDF document

[62] Z. T. Taylor, K . Gowri, S. Katipamula, "GridLAB-D Technical Support Document: Residential End-Use Module Version 1.0," Technical Report-PNNL- 17694 [Online] Available: Temporary on-line reference link removed - see the PDF document

[63] R. G. Pratt, C. C. Conner, E. E. Richman, K. G. Ritland, W. F. Sandusky, and M. E. Taylor, "Description of Electric Energy Use in Single Family Residences in the Pacific Northwest," DOE/BP 13795 21, Bonneville Power Administration, Portland, OR, 1989. [Online] Available: Temporary on-line reference link removed - see the PDF document

[64] EN 50160, voltage characteristics of electricity supplied by public distribution systems, 1999.

[65] S. Shao, M. Pipattanasomporn, and S. Rahman, "Grid Integration of Electric Vehicles and Demand Response With Customer Choice," IEEE Trans. Smart Grid., vol. 3, no. 1, pp. 543–550, 2012.
[CrossRef] [Web of Science Times Cited 166] [SCOPUS Times Cited 200]


[66] FHA, "Summary of Travel Trends: 2009 National Household Travel Survey," p. 82, 2011.

[67] J. D. Dogger, B. Roossien, and F. D. J. Nieuwenhout, "Characterization of Li-ion batteries for intelligent management of distributed grid connected storage," IEEE Trans. Energy Convers., vol. 26, no. 1, pp. 256– 263, 2011.
[CrossRef] [Web of Science Times Cited 81] [SCOPUS Times Cited 99]


[68] E. Bompard, E. Carpaneto, G. Chicco, and R. Napoli, "Convergence of the backward / forward sweep method for the load-flow analysis of radial distribution systems," Int. J. of Elect. Power & Energy Syst., vol. 22, pp. 521–530, 2000.
[CrossRef] [Web of Science Times Cited 57]


[69] R.-E. Precup, R.-C. David, E. M. Petriu, M.-B. Radac, S. Preitl, J. Fodor, "Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems," Knowledge-Based Systems, vol. 38, no. 9, pp. 74-84, 2013.
[CrossRef] [Web of Science Times Cited 63] [SCOPUS Times Cited 74]


[70] D. Zaharie, "Influence of crossover on the behavior of Differential Evolution Algorithms," Applied Soft Computing, vol. 9, no. 3, pp. 1126-1138, 2009.
[CrossRef] [Web of Science Times Cited 164] [SCOPUS Times Cited 198]


[71] A. W.Mohamed, H. Z. Sabry, M. Khorshid, "An alternative differential evolution algorithm for global optimization," Journal of Advanced Research, vol. 3, no. 2, pp. 149-165, 2012.
[CrossRef] [SCOPUS Times Cited 44]


[72] D. Karaboga B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Applied Soft Computing, vol. 8, no. 1, pp. 687–697, 2008.
[CrossRef] [Web of Science Times Cited 1623] [SCOPUS Times Cited 2170]




References Weight

Web of Science® Citations for all references: 29,914 TCR
SCOPUS® Citations for all references: 9,539 TCR

Web of Science® Average Citations per reference: 410 ACR
SCOPUS® Average Citations per reference: 131 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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