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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2017 - 8

Particle Swarm Optimization with Power-Law Parameter Based on the Cross-Border Reset Mechanism

WANG, H. See more information about WANG, H. on SCOPUS See more information about WANG, H. on IEEExplore See more information about WANG, H. on Web of Science, FEI, Y. See more information about  FEI, Y. on SCOPUS See more information about  FEI, Y. on SCOPUS See more information about FEI, Y. on Web of Science, LI, Y. See more information about  LI, Y. on SCOPUS See more information about  LI, Y. on SCOPUS See more information about LI, Y. on Web of Science, REN, S. See more information about  REN, S. on SCOPUS See more information about  REN, S. on SCOPUS See more information about REN, S. on Web of Science, CHE, J. See more information about  CHE, J. on SCOPUS See more information about  CHE, J. on SCOPUS See more information about CHE, J. on Web of Science, XU, H. See more information about XU, H. on SCOPUS See more information about XU, H. on SCOPUS See more information about XU, H. on Web of Science
 
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Author keywords
evolutionary computation, optimization, particle swarm optimization, performance evaluation, benchmark testing

References keywords
swarm(23), optimization(22), algorithm(10), levy(9), evolutionary(9), computation(8), intelligence(6), flight(6), computing(6), applied(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-11-30
Volume 17, Issue 4, Year 2017, On page(s): 59 - 68
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04008
Web of Science Accession Number: 000417674300008
SCOPUS ID: 85035775186

Abstract
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In order to improve the performance of traditional particle swarm optimization, this paper introduces the principle of Levy flight and cross-border reset mechanism. In the proposed particle swarm optimization, the dynamic variation of parameters meets the power-law distribution and the pattern of particles transition conforms to the Levy flight in the process of algorithm optimization. It means the particles make long distance movements in the search space with a small probability and make short distance movements with a large probability. Therefore, the particles can jump out of local optimum more easily and coordinate the global search and local search of particle swarm optimization. This paper also designs the cross-border reset mechanism to make particles regain optimization ability when stranding on the border of search space after a long distance movement. The simulation results demonstrate the proposed algorithms are easier to jump out of local optimum and have higher accuracy when compared with the existing similar algorithms based on benchmark test functions and handwriting character recognition system.


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

[1] X. S. Yang, "Swarm intelligence based algorithms: a critical analysis", Evolutionary Intelligence, vol. 7, no. 1, pp. 17-28, 2014.
[CrossRef] [SCOPUS Times Cited 22]


[2] Rini D. P., Shamsuddin S. M., "Particle Swarm Optimization: Technique, System and Challenges", International Journal of Computer Applications, vol.1, no. 1, pp. 33-45, 2011.
[CrossRef]


[3] Saini S., Rambli D. R. B. A., Zakaria N., et al, "A Review on Particle Swarm Optimization Algorithm and Its Variants to Human Motion Tracking", Mathematical Problems in Engineering, vol. 2014, pp. 13-14, 2014.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 7]


[4] Devadoss S., Luckstead J., Danforth D., et al, "The power-law distribution for lower tail cities in India", Physica A Statistical Mechanics & Its Applications, vol. 442, pp. 193-196, 2016.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8]


[5] Thelwall M., "The discretised lognormal and hooked power-law distributions for complete citation data: Best options for modelling and regression", Journal of Informetrics, vol.10, no.2, pp. 336-346, 2016.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 16]


[6] Yang X. S., "Efficiency Analysis of Swarm Intelligence and Randomization Techniques", Journal of Computational & Theoretical Nanoscience, vol. 9, no. 2, pp. 189-198, 2012.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 22]


[7] Odili J. B., Kahar M. N. M., Anwar S., "African Buffalo Optimization: A Swarm-Intelligence Technique", Procedia Computer Science, vol. 76, pp. 443-448, 2015.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 8]


[8] Arpan Kumar Kar, "Bio inspired computing - A review of algorithms and scope of applications", Expert Systems with Applications, vol. 59, pp. 20-32, no. C, 2016.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 32]


[9] Li Q., Zhang C., Chen P., "An improved ant colony algorithm based on particle swarm optimization", Control and Decision, vol. 6, pp. 873-878, 2013.
[CrossRef]


[10] Mandal D., Ghoshal S. P., Bhattacharjee A. K., "Design of Concentric Circular Antenna Array with Central Element Feeding Using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach and Evolutionary Programing Technique", Journal of Infrared Millimeter & Terahertz Waves, vol. 31, no. 6, pp. 667-680, 2010.
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 53]


[11] Jianan Lu, Yonghua Chen, "Particle Swarm Optimization (PSO) Based Topology Optimization of Part Design with Fuzzy Parameter Tuning", Computer-Aided Design and Applications, vol. 11, no. 1, pp. 62-68, 2013.
[CrossRef] [SCOPUS Times Cited 3]


[12] Mehdinejad M., Mohammadi-Ivatloo B., et al, "Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms", International Journal of Electrical Power & Energy Systems, vol. 83, pp. 104-116, 2016.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 7]


[13] Shi Y., Eberhart R., "Modified particle swarm optimizer", IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, vol. 6, pp. 69-73, 1998.
[CrossRef] [Web of Science Times Cited 1085]


[14] Shi Y., Eberhart R., "Fuzzy adaptive particle swarm optimization", Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 101-106, 2001.
[CrossRef]


[15] Clerc M, "The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. Evolutionary Computation", Proceedings of the 1999 Congress on Evolutionary Computation, vol. 3, pp. 1951-1957, 1999.
[CrossRef] [SCOPUS Times Cited 1203]


[16] Chatterjee A., Siarry P., "Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization", Computers & Operations Research", vol. 33, no. 3, pp. 859-871, 2006.
[CrossRef] [Web of Science Times Cited 266] [SCOPUS Times Cited 405]


[17] Suganthan P. N., "Particle swarm optimiser with neighbourhood operator", Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on IEEE, vol. 3, pp. 1958-1962, 1999.
[CrossRef] [SCOPUS Times Cited 606]


[18] Ratnaweera A., Halgamuge S. K., Watson H. C., "Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients", IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 240-255, 2004.
[CrossRef] [Web of Science Times Cited 1312] [SCOPUS Times Cited 1774]


[19] Liu J. Y., Guo M. Z., Deng C., "Particle swarm optimization algorithm based on goose", Computer science, vol.33, no. 11, pp. 166-168, 2006.
[CrossRef]


[20] Chen H., Zhu Y., "Optimization based on symbiotic multi-species coevolution", Applied Mathematics & Computation, vol. 205, no. 1, pp. 47-60, 2008.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 30]


[21] Qin Q. D., Li R. J., "Two-population particle swarm optimization algorithm based on bioparasitic behavior", Control and Decision, vol. 26, no. 4, pp. 548-552, 2011.
[CrossRef]


[22] Yang P., Sun Y. M., Liu X. L., "The particle swarm optimization algorithm based on bacterial foraging chemotactic operator", Application Research of Computers, vol. 28, no. 10, pp. 3640-3642, 2011.
[CrossRef]


[23] Viswanathan G. M., Afanasyev V., Buldyrev S. V., "Levy flight search patterns of wandering albatrosses", Nature, vol. 381, no. 6581, pp. 413-415, 1996.
[CrossRef] [Web of Science Times Cited 645]


[24] Shlesinger M. F., Klafter J., "Levy Walks Versus Levy Flights On Growth and Form", Springer Netherlands, vol. 100, pp. 279-283, 1986.
[CrossRef]


[25] Richer T. J., Blackwell T. M., "The Levy Particle Swarm", Evolutionary Computation, 2006. CEC 2006. IEEE Congress on. IEEE, 2006, pp. 808-815, 2006.
[CrossRef] [Web of Science Times Cited 33]


[26] Wang D., Tang C. Q., Tian B G, Qu L. S., Zhang J. C., Di Z. R., "The Levy flight and Brownian motion characteristic cycle of competition game and stable species coexistence conditions", Acta Physica Sinica, vol. 16, pp. 439-446, 2014.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Record]


[27] Li D., "Cooperative quantum-behaved particle swarm optimization with dynamic varying search areas and Levy flight disturbance", The scientific world journal, vol. 2014, 2014.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 2]


[28] Yan X. F., Ye D. Y., "An improved algorithm of bacteria foraging based on the Levy flight", Computer Systems & Applications, vol. 24, no. 3, pp. 124-132, 2015.
[CrossRef]


[29] Hakli H., Uguz H., "A novel particle swarm optimization algorithm with Levy flight", Applied Soft Computing, vol. 23, no. 5, pp. 333,345, 2014.
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 38]


[30] Martin D., Caballero B., Haber R., "Optimal Tuning of a Networked Linear Controller Using a Multi-Objective Genetic Algorithm. Application to a Complex Electromechanical Process", International Journal of Innovative Computing Information & Control Ijicic, vol.5, pp. 3405-3414, 2009.
[CrossRef] [SCOPUS Times Cited 4]


[31] Harmanani H. M., Drouby F., Ghosn S. B., "A parallel genetic algorithm for the open-shop scheduling problem using deterministic and random moves", International Journal of Artificial Intelligence, vol. 14, no. 1, pp. 130-144, 2016.
[CrossRef]


[32] Castillo O., Neyoy H., Soria J., "A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot", Applied Soft Computing Journal, vol. 28, pp. 150-159, 2015.
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 41]


[33] Precup R. E., Sabau M. C., Petriu E. M., "Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for anti-lock braking systems", Applied Soft Computing, vol. 27, pp. 575-589, 2015.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 23]


[34] Jensi R., Jiji G. W., "An enhanced particle swarm optimization with Levy flight for global optimization", Applied Soft Computing, vol. 43, pp. 248-261, 2016.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 15]


[35] Manesh M. H. K., Ameryan M., "Optimal design of a solar-hybrid cogeneration cycle using Cuckoo Search algorithm", Applied Thermal Engineering, vol. 102, pp. 1300-1313, 2016.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 6]


[36] Suganthan P. N., Hansen N., Liang J. J., et al. "Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization". Nanyang Technological University, 2005, 1-50.

[37] Mallipeddi R., Suganthan P. N. "Problem definitions and evaluation criteria for the CEC 2010 competition on constrained real-parameter optimization". Nanyang Technological University, 2010, 1-17.



References Weight

Web of Science® Citations for all references: 3,565 TCR
SCOPUS® Citations for all references: 4,325 TCR

Web of Science® Average Citations per reference: 94 ACR
SCOPUS® Average Citations per reference: 114 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

Citations for references updated on 2018-01-24 08:29 in 230 seconds.




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