LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: IEEE
Languages: English
Types: Article
Subjects:

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_PATTERNRECOGNITION, ComputingMethodologies_ARTIFICIALINTELLIGENCE, MathematicsofComputing_NUMERICALANALYSIS
Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Netw., vol. 4. Perth, WA, Australia, 1995, pp. 1942-1948.
    • [2] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proc. 6th Int. Symp. Micro Mach. Human Sci., Nagoya, Japan, 1995, pp. 39-43.
    • [3] Y. del Valle, G. K. Venayagamoorthy, S. Mohagheghi, J.-C. Hernandez, and R. G. Harley, “Particle swarm optimization: Basic concepts, variants and applications in power systems,” IEEE Trans. Evol. Comput., vol. 12, no. 2, pp. 171-195, Apr. 2008.
    • [4] M. R. AlRashidi and M. E. El-Hawary, “A survey of particle swarm optimization applications in electric power systems,” IEEE Trans. Evol. Comput., vol. 13, no. 4, pp. 913-918, Aug. 2009.
    • [5] R. Ruiz-Cruz, E. N. Sanchez, F. Ornelas-Tellez, A. G. Loukianov, and R. G. Harley, “Particle swarm optimization for discrete-time inverse optimal control of a doubly fed induction generator,” IEEE Trans. Cybern., vol. 43, no. 6, pp. 1698-1709, Dec. 2013.
    • [6] R. V. Kulkarni and G. K. Venayagamoorthy, “Particle swarm optimization in wireless-sensor networks: A brief survey,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 41, no. 2, pp. 262-267, Mar. 2011.
    • [7] B. Xue, M. Zhang, and W. N. Browne, “Particle swarm optimization for feature selection in classification: A multi-objective approach,” IEEE Trans. Cybern., vol. 43, no. 6, pp. 1656-1671, Dec. 2013.
    • [8] S. Janson and M. Middendorf, “A hierarchical particle swarm optimizer and its adaptive variant,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 35, no. 6, pp. 1272-1282, Dec. 2005.
    • [9] F. van den Bergh and A. P. Engelbrecht, “A cooperative approach to particle swarm optimization,” IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 225-239, Jun. 2004.
    • [10] J. J. Liang and P. N. Suganthan, “Dynamic multi-swarm particle swarm optimizer,” in Proc. IEEE Swarm Intell. Symp., Pasadena, CA, USA, 2005, pp. 124-129.
    • [11] Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proc. IEEE Int. Conf. Evol. Comput., Anchorage, AK, USA, 1998, pp. 69-73.
    • [12] M. Clerc and J. Kennedy, “The particle swarm-Explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans. Evol. Comput., vol. 6, no. 1, pp. 58-73, Feb. 2002.
    • [13] A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 240-255, Jun. 2004.
    • [14] Z.-H. Zhan, J. Zhang, Y. Li, and H. S.-H. Chung, “Adaptive particle swarm optimization,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 39, no. 6, pp. 1362-1381, Dec. 2009.
    • [15] X.-H. Shi, Y.-C. Liang, H.-P. Lee, C. Liu, and L. M. Wang, “An improved GA and a novel PSO-GA-based hybrid algorithm,” Inf. Process. Lett., vol. 93, no. 5, pp. 255-261, Mar. 2005.
    • [16] Y.-T. Kao and E. Zahara, “A hybrid genetic algorithm and particle swarm optimization for multimodal functions,” Appl. Soft Comput., vol. 8, no. 2, pp. 849-857, Mar. 2008.
    • [17] F. Valdez, P. Melin, O. Castillo, and O. Montiel, “A new evolutionary method with a hybrid approach combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making,” in Proc. IEEE Congr. Evol. Comput., Hong Kong, 2008, pp. 1333-1339.
    • [18] K. Premalatha and A. M. Natarajan, “Hybrid PSO and GA for global maximization,” Int. J. Open Prob. Compt. Math., vol. 2, no. 4, pp. 597-608, Dec. 2009.
    • [19] C.-F. Juang, “A hybrid of genetic algorithm and particle swarm optimization for recurrent network design,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 2, pp. 997-1006, Apr. 2004.
    • [20] F. Grimaccia, M. Mussetta, and R. E. Zich, “Genetical swarm optimization: Self-adaptive hybrid evolutionary algorithm for electromagnetics,” IEEE Trans. Antennas Propag., vol. 55, no. 3, pp. 781-785, Mar. 2007.
    • [21] W.-T. Li, X.-W. Shi, Y.-Q. Hei, S.-F. Liu, and J. Zhu, “A hybrid optimization algorithm and its application for conformal array pattern synthesis,” IEEE Trans. Antennas Propag., vol. 58, no. 10, pp. 3401-3406, Oct. 2010.
    • [22] S. Jeong, S. Hasegawa, K. Shimoyama, and S. Obayashi, “Development and investigation of efficient GA/PSO-HYBRID algorithm applicable to real-world design optimization,” IEEE Comput. Intell. Mag., vol. 4, no. 3, pp. 36-44, Aug. 2009.
    • [23] C.-S. Zhang, J.-X. Ning, S. Lu, D.-T. Ouyang, and T.-N. Ding, “A novel hybrid differential evolution and particle swarm optimization algorithm for unconstrained optimization,” Oper. Res. Lett., vol. 37, no. 2, pp. 117-122, Mar. 2009.
    • [24] S. Li, M. Tan, I. W. Tsang, and J. T.-Y. Kwok, “A hybrid PSO-BFGS strategy for global optimization of multimodal functions,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 41, no. 4, pp. 1003-1014, Aug. 2011.
    • [25] Z.-H. Liu, J. Zhang, S.-W. Zhou, X.-H. Li, and K. Liu, “Coevolutionary particle swarm optimization using AIS and its application in multiparameter estimation of PMSM,” IEEE Trans. Cybern., vol. 43, no. 6, pp. 1921-1935, Dec. 2013.
    • [26] R. Mendes, J. Kennedy, and J. Neves, “The fully informed particle swarm: Simpler, maybe better,” IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 204-210, Jun. 2004.
    • [27] J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Trans. Evol. Comput., vol. 10, no. 3, pp. 281-295, Jun. 2006.
    • [28] Z.-H. Zhan, J. Zhang, Y. Li, and Y.-H. Shi, “Orthogonal learning particle swarm optimization,” IEEE Trans. Evol. Comput., vol. 15, no. 6, pp. 832-847, Dec. 2011.
    • [29] C.-H. Li, S.-X. Yang, and T. T. Nguyen, “A self-learning particle swarm optimizer for global optimization problems,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 3, pp. 627-646, Jun. 2012.
    • [30] Z.-H. Ren, A.-M. Zhang, C.-Y. Wen, and Z.-R. Feng, “A scatter learning particle swarm optimization algorithm for multimodal problems,” IEEE Trans. Cybern., vol. 44, no. 7, pp. 1127-1140, Jul. 2014.
    • [31] G. E. Robinson and R. E. Page, “Genetic determination of nectar foraging, pollen foraging, and nest-site scouting in honey bee colonies,” Behav. Ecol. Sociobiol., vol. 24, no. 5, pp. 317-323, May 1989.
    • [32] P. Berthold and F. Pulido, “Heritability of migratory activity in a natural bird population,” Proc. Biol. Sci., vol. 257, no. 1350, pp. 311-315, Sep. 1994.
    • [33] S. M. Stigler, “Darwin, Galton and the statistical enlightenment,” J. Roy. Stat. Soc. A., vol. 173, no. 3, pp. 469-482, Jul. 2010.
    • [34] T. J. Bazzett, An Introduction to Behavior Genetics. Sunderland, MA, USA: Sinauer Assoc., 2008.
    • [35] N. E. Raine, T. C. Ings, A. Dornhaus, N. Saleh, and L. Chittka, “Adaptation, genetic drift, pleiotropy, and history in the evolution of bee foraging behavior,” in Advances in the Study of Behavior. New York, NY, USA: Academic Press, 2006, pp. 305-354.
    • [36] J. Alcock, Animal Behavior: An Evolutionary Approach, 5th ed. Sunderland, MA, USA: Sinauer Assoc., 1993.
    • [37] K. Sterelny, “Made by each other: Organisms and their environment,” Biol. Philos., vol. 20, no. 1, pp. 21-36, Jan. 2005.
    • [38] C. G. Jones, J. H. Lawton, and M. Shachak, “Positive and negative effects of organisms as physical ecosystem engineers,” Ecology, vol. 78, no. 7, pp. 1946-1957, 1997.
    • [39] S. H. Ling et al., “Hybrid particle swarm optimization with wavelet mutation and its industrial applications,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 38, no. 3, pp. 743-763, Jun. 2008.
    • [40] M. S. Arumugam and M. V. C. Rao, “On the improved performances of the particle swarm optimization algorithms with adaptive parameters, crossover operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems,” Appl. Soft Comput., vol. 8, no. 1, pp. 324-336, 2008.
    • [41] F. Galton, Hereditary Genius. London, U.K.: Macmillan, 1869.
    • [42] J. L. Fuller and W. R. Thompson, Behavior Genetics. New York, NY, USA: Wiley, 1960.
    • [43] (Sep. 10, 2015). GLPSO Source Code. [Online]. Available: http://www.ai.sysu.edu.cn/GYJ/glpso/c_code/
    • [44] X. Yao, Y. Liu, and G.-M. Lin, “Evolutionary programming made faster,” IEEE Trans. Evol. Comput., vol. 3, no. 2, pp. 82-102, Jul. 1999.
    • [45] J. J. Liang, B. Y. Qu, P. N. Suganthan, and A. G. Hernández-Díaz, “Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization,” Comput. Intell. Lab., Zhengzhou Univ., Zhengzhou, China, Tech. Rep. 201212, 2013.
    • [46] H. Gao and W.-B. Xu, “A new particle swarm algorithm and its globally convergent modifications,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 41, no. 5, pp. 1334-1350, Oct. 2011.
    • [47] T. Blackwell, “A study of collapse in bare bones particle swarm optimization,” IEEE Trans. Evol. Comput., vol. 16, no. 3, pp. 354-372, Jun. 2012.
    • [48] W.-J. Yu et al., “Differential evolution with two-level parameter adaptation,” IEEE Trans. Cybern., vol. 44, no. 7, pp. 1080-1099, Jul. 2014.
    • [49] M. Ergezer and D. Simon, “Mathematical and experimental analyses of oppositional algorithms,” IEEE Trans. Cybern., vol. 44, no. 11, pp. 2178-2189, Nov. 2014.
    • [50] J. Derrac, S. García, D. Molina, and F. Herrera, “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms,” Swarm Evol. Comput., vol. 1, no. 1, pp. 3-18, Mar. 2011.
    • [51] K. Socha and M. Dorigo, “Ant colony optimization for continuous domains,” Eur. J. Oper. Res., vol. 185, no. 3, pp. 1155-1173, Mar. 2008.
    • [52] A. K. Qin, V. L. Huang, and P. N. Suganthan, “Differential evolution algorithm with strategy adaptation for global numerical optimization,” IEEE Trans. Evol. Comput., vol. 13, no. 2, pp. 398-417, Apr. 2009.
    • [53] P. Larrañaga, R. Etxeberria, J. A. Lozano, and J. M. Peña, “Optimization in continuous domains by learning and simulation of Gaussian networks,” in Proc. Genet. Evol. Comput. Conf., Las Vegas, NV, USA, 2000, pp. 201-204.
    • [54] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm,” J. Glob. Optim., vol. 39, no. 3, pp. 459-471, Nov. 2007.
    • [55] N. Hansen, S. D. Müller, and P. Koumoutsakos, “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES),” Evol. Comput., vol. 11, no. 1, pp. 1-18, 2003.
    • [56] X.-L. Hu and J. Wang, “An improved dual neural network for solving a class of quadratic programming problems and its k-winners-take-all application,” IEEE Trans. Neural Netw., vol. 19, no. 12, pp. 2022-2031, Dec. 2008.
    • He has 200 publications and he is a Chartered Engineer. Dr. Li is an Associate Editor of the IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION and the SM Journal of Engineering Sciences. Jun Zhang (M'02-SM'08) received the Ph.D. degree in electronic engineering from the City University of Hong Kong, Hong Kong, in 2002. He is currently a Changjiang Chair Professor with the Department of Computer Science, Sun Yat-sen University, Guangzhou, China. His current research interests include computational intelligence, cloud computing, data mining, and power electronic circuits. He has published over 200 technical papers in his research area. Dr. Zhang was a recipient of the China National Funds for Distinguished Young Scientists Award from the National Natural Science Foundation of China in 2011 and the First-Grade Award in Natural Science Research from the Ministry of Education, China, in 2009. He is currently an Associate Editor of the IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, and the IEEE TRANSACTIONS ON CYBERNETICS.
  • No related research data.
  • No similar publications.

Share - Bookmark

Download from

Cite this article