Enhancement of the Multi-Particle Collision Algorithm by mechanisms derived from the Opposition-Based Optimization

• Hernández Torres, Reynier ^[1] ; Campos Velho, Haroldo F. ^[1] ; P. da Luz, Eduardo F. ^[2]
  1. [1] Instituto Nacional de Pesquisas Espaciais

     Instituto Nacional de Pesquisas Espaciais

     Brasil

     
  2. [2] Centro Nacional de Monitoramento e Alertas de Desastres Naturais (Cemaden), Sao José dos Campos, Sao Paulo, Brazil
• Localización: Selecciones Matemáticas, ISSN-e 2411-1783, Vol. 6, Nº. 2, 2019 (Ejemplar dedicado a: Agosto-Diciembre), págs. 156-177
• Idioma: inglés
• DOI: 10.17268/sel.mat.2019.02.03
• Enlaces
  • Texto completo (pdf)
• Resumen

  • New versions of the metaheuristic Multi-Particle Collision Algorithm (MPCA) are presented. In order to provide more effective candidate solutions for an optimization problem, the concept of opposition and reflection is introduced to improve the capacity to find a solution in the search space. Four different strategies to compute the reflected and opposite points are implemented. The performance of all implementations is evaluated over thirty objective functions with different complexities, using serial and parallel versions of the algorithms.

• Referencias bibliográficas
  • Ahandani, M. A. Opposition-based learning in the shuffled bidirectional differential evolution algorithm. Swarm and Evolutionary Computation,...
  • Al-Qunaieer, F. S., Tizhoosh, H. R., Rahnamayan, S. Opposition based computing–a survey. 2010 International Joint Conference on Neural Networks...
  • Aluffi-Pentini, F., Parisi, V., Zirilli, F. Global optimization and stochastic differential equations. Journal of optimization theory and...
  • Anochi, J. A. and Campos Velho, H. F. Optimization of feedforward neural network by Multiple Particle Collision Algorithm. 2014 IEEE Symposium...
  • Anochi, J. A., Campos Velho, H. F., Furtado, H. C. M., Luz, E. F. P. Self-configuring Two Types of Neural Networks by MPCA. Journal of Mechanics...
  • Antoniou, A., Lu, W.-S. Practical optimization: algorithms and engineering applications. Springer Science & Business Media, 2007.
  • Beyer, H.-G. The theory of evolution strategies. Springer Science & Business Media, 2013.
  • Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V. Self-adapting control parameters in differential evolution: a comparative study...
  • Chelouah, R., Siarry, P. A continuous genetic algorithm designed for the global optimization of multimodal functions. Journal of Heuristics...
  • Das, S., Biswas, A., Dasgupta, S., Abraham, A. Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications....
  • Das, S., Suganthan, P. N. Differential evolution: a survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1),...
  • De Castro, L. N., Timmis, J. Artificial immune systems: a new computational intelligence approach. Springer Science & Business Media,...
  • Mendiburu, F., Simon, R. Agricolae-ten years of an open source statistical tool for experiments in breeding, agriculture and biology. Technical...
  • Dorigo, M. and Birattari, M. Ant colony optimization. Encyclopedia of machine learning, Springer (2010) 36–39.
  • Dorigo, M., Birattari, M., St¨utzle, T. Ant colony optimization. Computational Intelligence Magazine, IEEE, 1(4) (2006) 28–39.
  • Du, K.-L., Swamy, M. N. S. Search and Optimization by Metaheuristics. Springer, 2016.
  • Eberhart, R. C., Kennedy, J. A new optimizer using particle swarm theory. Proceedings of the sixth international symposium on micro machine...
  • Echevarría, L. C., Llanes Santiago, O., Silva Neto, A. J. Aplicación de los algoritmos Evolución Diferencial y Colisión de Partículas al diagnóstico...
  • Ergezer, M., Simon, D., Du, D. Oppositional biogeography-based optimization. IEEE International Conference on Systems, Man and Cybernetics...
  • Fogel, L. J. Intelligence Through Simulated Evolution: Forty Years of Evolutionary Programming. John Wiley & Sons, Inc., New York, 1999.
  • Gao, W.-F., Liu, S.-Y. A modified artificial bee colony algorithm. Computers & Operations Research, 39(3) (2012) 687–697.
  • Gao, W.-F., Liu, S.-Y., Huang, L.-L. A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE...
  • Gao,W.-F., Liu, S.-Y., Huang, L.-L. A novel artificial bee colony algorithm with powell’s method. Applied Soft Computing, 13(9) (2013), 3763–3775.
  • Gao, W., Chan, F. T. S., Huang, L. L., Liu, S. Bare bones artificial bee colony algorithm with parameter adaptation and fitness-based neighborhood....
  • Geem, Z. W., Kim, J. H., Loganathan, G. V. A new heuristic optimization algorithm: harmony search. Simulation, 76(2) (2001) 60–68.
  • Guo, Z., Wang, S., Yue, X., Yang,H. Global harmony search with generalized opposition-based learning. Soft Computing, 21(8) (2017), 2129–2137.
  • Guo, Z., Yue, X., Zhang, K., Deng, C., Liu, S. Enhanced social emotional optimisation algorithm with generalised opposition–based learning....
  • Han, L., He, X. A novel opposition-based particle swarm optimization for noisy problems. Third International Conference on Natural Computation...
  • Holland, J. H. Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA, USA, 1992.
  • Jamil, M., Yang, X.-S. A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical...
  • Fister Jr., I., Yang, X.-S., Fister, I., Brest, J., Fister, D. A brief review of nature-inspired algorithms for optimization. CoRR, abs/1307.4186,...
  • Kalra, S., Sriram, A., Rahnamayan, S., Tizhoosh, H. R. Learning opposites using neural networks. CoRR, abs/1609.05123, 2016.
  • Kang, F., Li, J., Ma, Z. Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Information Sciences,...
  • Karaboga, D. An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes university, Engineering...
  • Karaboga, D., Basturk, B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal...
  • Kennedy, J. Particle swarm optimization. Encyclopedia of Machine Learning, Springer (2010) 760–766
  • Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. Optimization by simulated annealing. Science 220(4598) (1983) 671–680.
  • Kuo, R. J., Zulvia, F. E. The gradient evolution algorithm: A new metaheuristic. Information Sciences, 316 (2015) 246–265.
  • Langdon, W. B., Gustafson, S. M. Genetic programming and evolvable machines: ten years of reviews. Genetic Programming and Evolvable Machines...
  • Liang, J. J., Qin, A. K., Suganthan, P. N., Baskar, S. Comprehensive learning particle swarm optimizer for global optimization of multimodal...
  • Liang, J. J., Qu, B. Y., Suganthan, P. N. Problem definitions and evaluation criteria for the CEC 2014 special session and competition on...