DOI:
https://doi.org/10.14483/2322939X.8007Publicado:
2014-12-17Número:
Vol. 11 Núm. 1 (2014)Sección:
Investigación y DesarrolloAnálisis de las condiciones iniciales para el algoritmo de optimización basado en enjambres partículas con comportamiento de vorticidad
Analysis of initial conditions for optimization algorithm based on particle swarm with vorticity behavior
Palabras clave:
Condiciones iniciales, enjambre de partículas, optimización. (es).Palabras clave:
Initial conditions, optimization, particle swarm. (en).Descargas
Resumen (es)
Este artículo analiza el efecto que tienen diferentes configuraciones de condiciones iniciales para el algoritmo de optimización basado en enjambres de partículas con comportamiento de vorticidad. El algoritmo propuesto combina la búsqueda basada en gradiente y un comportamiento de enjambre de partículas, por lo cual, este algoritmo puede ser afectado por las condiciones iniciales dadas para las partículas. Para observar las características del algoritmo se emplea una función de prueba 2D.
Resumen (en)
This paper analyzes the effect of different configurations of initial conditions for the optimization algorithm based on particle swarms with vorticity behavior. The proposed algorithm combines the gradient-based search and particle swarm behavior, thus, this algorithm can be affected by the initial conditions for the particles. Finally, a 2D test function is used to observe the characteristics of the algorithm.
Referencias
U. Erdmann, W. Ebeling, L. Schimansky, A. Ordemann, F. Moss, “Active brownian particle and random walk theories of the motions of zooplankton: application to experiments with swarms of daphnia”, Journal of Theoretical Biology 9, February. 2008.
Y. Hsin, “Emergence of vortex swarming in daphnia”, Term Paper for Emergent State of Matter, Spring 2006.
E. Werner, “Nonequilibrium statistical mechanics of swarms of driven particles”, Elsevier Physica. 2002.
M. Dorigo, M. Birattari, T. Stützle, “Ant Colony Optimization. Artificial Ants as a Computational Intelligence Technique”, IEEE Computational Intelligence Magazine, November. 2006.
A. Zecchin, A. Simpson, H. Maier, J. Nixon, “Parametric Study for an Ant Algorithm Applied Water Distribution System Optimization”, IEEE Transactions On Evolutionary Computation, Vol. 9, No. 2, April. 2005.
D. Karaboga, “An Idea Based On Honey Bee Swarm For Numerical Optimization”, Technical Report-TR06, October. 2005.
S. Thakoor, J. Morookian, C. Javaan, B. Hine, S. Zornetzer, “BEES: Exploring Mars with Bioinspired Technologies”, IEEE Computer Society. 2004.
K. Passino, “Biomimicry of bacterian foragin for distributed optimization and control”, IEEE Control Systems Magazine, June. 2002.
E. Russell, K. James, “Particle Swarm Optimization”, IEEE Proceedings Neural Networks. 1995.
L. Coelho, “A quantum particle swarm optimizer with chaotic mutation operator”, Elsevier Chaos Solitons and Fractals. 2006.
L. Coelho, V. Cocco, “Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valvepoint effects”, Elsevier Energy Conversion and Management. 2008.
D. Sedighizadeh, E. Masehian, “Particle swarm optimization methods, taxonomy and applications”, International Journal of Computer Theory and Engineering, Vol. 1, No. 5, December. 2009.
X. Yang, “A new metaheuristic bat-inspired algorithm”, Nature Inspired Cooperative Strategies for Optimization (NICSO). 2010.
X. Yang, Firefly algorithm, lévy flights and global optimization, Springer London: Research and Development in Intelligent Systems XXVI, 2009.
A. Mucherino, O. Seref, Monkey search: a novel metaheuristic search for global optimization, in Aip Conference Proceedings,
Data mining systems analysis and optimization in biomedicine. 2007.
A. Kaveh, S. Talatahari, “A novel heuristic optimization method: charged system search”, Springer-Verlag, Acta Mechanica. 2010.
X. Yang, Harmony search as a metaheuristic algorithm, Springer Berlin: Music- Inspired Harmony Search Algorithm, 2009.
H. Shah, “Problem solving by intelligent water drops”, IEEE Congress on Evolutionary Computation. 2007.
D. Bratton, J. Kennedy, “Defining a Standard for Particle Swarm Optimization”, IEEE Swarm Intelligence Symposium SIS. 2007.
G. Evers, An automatic regrouping mechanism to deal with stagnation in particle swarm optimization, Master Thesis: University of Texas-Pan American, 2009.
J. Schutte, Particle swarms in sizing and global optimization, Master’s Dissertation: University of Pretoria, 2002.
M. Hvass, Tuning&Simplifying Heuristical Optimization, Ph.D. Thesis: University of Southampton, UK, 2010.
F. Van den Bergh, An Analysis of Particle Swarm Optimizers, PhD. Thesis: University of Pretoria, Pretoria, 2001.
E. Mesa, Supernova: un algoritmo novedoso de optimización global, Tesis de Maestría, Universidad Nacional de Colombia:
Sede Medellín, 2010.
K. Krishnanand, D. Ghose, “Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions”, Springer Science, Swarm Intell. 2009.
K. Passino, Biomimicry for optimization, control, and automation, Springer-Verlag: London, UK, 2005.
C. Feng, S. Cong, X. Feng, “A new adaptive inertia weight strategy in particle swarm optimization”, IEEE Congress on
Evolutionary Computation (CEC), 2007.
L. Yin, X. Liu, A PSO Algorithm Based on Biologe Population Multiplication (PMPSO), in Second Symposium International
Computer Science and Computational Technology (ISCSCT ’09), 2009.
J. García, E. Alba, Restart Particle Swarm Optimization with Velocity Modulation: A Scalability Test, in Springer, Soft Computing - A Fusion of Foundations, Methodologies and Applications, Vol. 1. 1997.
T. Hendtlass, “A particle swarm algorithm for high dimensional, multi-optima problem spaces”, IEEE Swarm Intelligence Symposium, 2005.
K. Parsopoulos, M. Vrahatis, “On the computation of all global minimizers through particle swarm optimization”, IEEE Transactions on Evolutionary Computation, Vol. 8. 2004.
J. Liang, A. Qin, P. Suganthan, S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions”, IEEE Transactions on Evolutionary Computation, Vol. 10. 2006.
J. Fieldsend, S. Singh, “A multiobjective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence”, Workshop on Computational Intelligence, Vol. 723/2003, pp. 34 - 44. 2002.
A. Cervantes, Clasificación mediante enjambre de prototipos, Tesis Doctoral: Universidad Carlos III de Madrid, Departamento
de Informática, Leganés, 2009.
K. Deb, N. Padhye, Development of efficient Particle Swarm Optimizers by using concepts from evolutionary algorithms, in
th annual conference on Genetic and evolutionary computation, GECCO ’10, pp. 55-62, 2010.
H. Liu, A. Abraham, Fuzzy adaptive turbulent particle swarm optimization, in V international conference on hybrid intelligent
systems (HIS’05), Rio de Janeiro, Brazil, November, 2005.
S. He, Q. Wu, J. Wen, J. Saunders, P. Patton, “A particle swarm optimizer with passive congregation”, Biosystems, Vol. 78, pp. 135 - 147. 2004.
J. Vlachogiannis, K. Lee, “A comparative study on particle swarm optimization for optimal steady-state performance
of power systems”, IEEE Transactions on Power Systems, Vol. 21,
No. 4, November. 2006.
A. Ordemanna, G. Balazsi, F. Moss, “Pattern formation and stochastic motion of the zooplankton Daphnia in a light field”, Elsevier Science B.V., Physica A 325. 2003.
M. Gómez, C. Danglot, L. Vega, “Sinopsis de pruebas estadísticas no paramétricas. Cuándo usarlas”, Revista
Mexicana de Pediatría, Vol. 70 No. 2 Marzo-Abril. 2003.
J. Derrac, S. García, D. Molina, H. Francisco, Un tutorial sobre el uso de test estadísticos no paramétricos en comparaciones
múltiples de metaheurísticas y algoritmos evolutivos, en VIII Congreso Espáñol Sobre Metauristicas, Algoritmos Evolutivos
y Bioinspirados, 2012.
S. García, A. Fernández, J. Luengo, F. Herrera, “Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power”, Information Sciences, Vol. 180, No. 20, pp. 2044–2064. 2010.
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