Impact of Parameter Tuning on the Cricket Chirping Algorithm

Impact of Parameter Tuning on the Cricket Chirping Algorithm

Автор: Jonti Deuri, S. Siva Sathya

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 9 vol.9, 2017 года.

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Most of the man-made technologies are nature-inspired including the popular heuristics or meta-heuristics techniques that have been used to solve complex computational optimization problems. In most of the meta-heuristics algorithms, adjusting the parameters has important significance to obtain the best performance of the algorithm. Cricket Chirping Algorithm (CCA) is a nature inspired meta-heuristic algorithm that has been designed by mimicking the chirping behavior of the cricket (insect) for solving optimization problems. CCA employs a set of parameters for its smooth functioning. In a meta-heuristic algorithm, controlling the values of various parameters is one of the most important issues of research. While solving the problem, the parameter values have a potential to improve the efficiency of the algorithm. The different parameters used in CCA are tuned for better performance of the algorithm through experiments conducted on a set of sample benchmark test functions and then, the fine-tuned CCA is compared with some other meta-heuristic algorithms. The results show the optimal choice of the various parameters to solve optimization problems using CCA.

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Metaheuristic Algorithm, Parameter Tuning, Optimization Problem, Cricket Chirping Algorithm, Test Function, Nature-inspired Algorithm

Короткий адрес: https://sciup.org/15010967

IDR: 15010967

Список литературы Impact of Parameter Tuning on the Cricket Chirping Algorithm

  • Nannen, Volker, Selmar Smit, and Agoston Eiben. "Costs and benefits of tuning parameters of evolutionary algorithms." Parallel Problem Solving from Nature–PPSN X (2008): 528-538. doi: 10.1007/978-3-540-87700-4_53
  • Fallahi, M., S. Amiri, and M. Yaghini. "A parameter tuning methodology for metaheuristics based on design of experiments." International Journal of Engineering and Technology Sciences 2, no. 6 (2014): 497-521.
  • Eiben, Agoston E., Zbigniew Michalewicz, Marc Schoenauer, and James E. Smith. "Parameter control in evolutionary algorithms." In Parameter setting in evolutionary algorithms, pp. 19-46. Springer Berlin Heidelberg, 2007. doi 10.1007/978-3-540-69432-8_2.
  • Holland, John H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1992.
  • DeJong, K. A. "Analysis of the Behavior of a Class of Genetic Adaptive Systems. Dept." Computer and Communication Sciences, University of Michigan, Ann Arbor (1975).
  • Grefenstette, John J. "Optimization of control parameters for genetic algorithms." IEEE Transactions on systems, man, and cybernetics 16, no. 1 (1986): 122-128. doi: 10.1109/TSMC.1986.289288
  • Yuan, Bo, and Marcus Gallagher. "A hybrid approach to parameter tuning in genetic algorithms." In Evolutionary Computation, 2005. The 2005 IEEE Congress on, vol. 2, pp.1096-1103. IEEE, 2005. doi: 10.1109/CEC.2005. 1554813
  • Back, Thomas. Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford university press, 1996.
  • Maron, Oded, and Andrew W. Moore. "The racing algorithm: Model selection for lazy learners." In Lazy learning, pp. 193-225. Springer Netherlands, 1997. doi 10.1007/978-94-017-2053-3_8.
  • Birattari, Mauro, Thomas Stützle, Luis Paquete, and Klaus Varrentrapp. "A racing algorithm for configuring metaheuristics." In Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation, pp. 11-18. Morgan Kaufmann Publishers Inc., 2002.
  • Poli, Riccardo, James Kennedy, and Tim Blackwell. "Particle swarm optimization." Swarm intelligence 1, no. 1 (2007): 33-57.doi: 10.1007/s11721-007-0002-0
  • Tewolde, Girma S., Darrin M. Hanna, and Richard E. Haskell. "Enhancing performance of PSO with automatic parameter tuning technique." In Swarm Intelligence Symposium, 2009. SIS'09. IEEE, pp. 67-73. IEEE, 2009. doi: 10.1109/SIS.2009.4937846
  • Dorigo, Marco, Vittorio Maniezzo, and Alberto Colorni. "Ant system: optimization by a colony of cooperating agents." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 26, no. 1 (1996): 29-41. doi: 10.1109/3477.484436
  • Wong, Kuan Yew. "Parameter tuning for ant colony optimization: a review." In Computer and Communication Engineering, 2008. ICCCE 2008. International Conference on, pp. 542-545. IEEE, 2008.
  • Kirkpatrick, Scott, C. Daniel Gelatt, and Mario P. Vecchi. "Optimization by simulated annealing." science 220, no. 4598 (1983): 671-680.
  • Geem, Zong Woo, Joong Hoon Kim, and G. V. Loganathan. "A new heuristic optimization algorithm: harmony search." Simulation 76, no. 2 (2001): 60-68.
  • Erol, Osman K., and Ibrahim Eksin. "A new optimization method: big bang–big crunch." Advances in Engineering Software 37, no. 2 (2006): 106-111.
  • Karaboga, Dervis, and Bahriye Basturk. "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm." Journal of global optimization 39, no. 3 (2007): 459-471. doi: 10.1007/s10898-007-9149-x
  • Karaboga, Dervis, and Bahriye Basturk. "On the performance of artificial bee colony (ABC) algorithm." Applied soft computing 8, no. 1 (2008): 687-697. doi.org/10.1016/j.asoc.2007.05.007
  • Rashedi, Esmat, Hossein Nezamabadi-Pour, and Saeid Saryazdi. "GSA: a gravitational search algorithm." Information sciences 179, no. 13 (2009): 2232-2248. doi:10.1016/j. ins.2009.03.004
  • Yang, Xin-She, and Suash Deb. "Cuckoo search via Lévy flights." In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, pp. 210-214. IEEE, 2009. doi: 10.1109/NABIC.2009.5393690
  • Yang, Xin-She. "A new metaheuristic bat-inspired algorithm." Nature inspired cooperative strategies for optimization (NICSO 2010) (2010): 65-74. doi 10.1007/978-3-642-12538-6_6
  • Yang, Xin-She. "Firefly algorithms for multimodal optimization." In International symposium on stochastic algorithms, pp. 169-178. Springer Berlin Heidelberg, 2009. doi: 10.1007/978-3-642-04944-6_14
  • Bouchibane, F. Z., and M. Bensebti. "Parameter tuning of Artificial Bee Colony algorithm for energy efficiency optimization in massive MIMO systems." Detection Systems Architectures and Technologies (DAT), Seminar on. IEEE, 2017 doi: 10.1109/DAT.2017.7889188 .
  • Kockanat, Serdar, and Nurhan Karaboga. "Parameter tuning of artificial bee colony algorithm for gaussian noise elimination on digital images." In Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on, pp. 1-4. IEEE, 2013.
  • Akay, Bahriye, and Dervis Karaboga. "Parameter tuning for the artificial bee colony algorithm." In International Conference on Computational Collective Intelligence, pp. 608-619. Springer Berlin Heidelberg, 2009.
  • Chebbi, Olfa, and Jouhaina Chaouachi. "Effective parameter tuning for genetic algorithm to solve a real world transportation problem." In Methods and Models in Automation and Robotics (MMAR), 2015 20th International Conference on, pp. 370-375. IEEE, 2015. doi: 10.1109/MMAR.2015.7283904.
  • Cooray, P. L. N. U., and Thashika D. Rupasinghe. "Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy-Minimizing Vehicle Routing Problem." Journal of Industrial Engineering 2017 (2017). doi.org/10.1155/2017/3019523
  • Hassanein, Osama I., Ayman A. Aly, and Ahmed A. Abo-Ismail. "Parameter tuning via genetic algorithm of fuzzy controller for fire tube boiler." International Journal of Intelligent Systems and Applications 4, no. 4 (2012): 9.
  • Mora-Melia, Daniel, Pedro L. Iglesias-Rey, F. Javier Martínez-Solano, and Pedro Muñoz-Velasco. "The Efficiency of Setting Parameters in a Modified Shuffled Frog Leaping Algorithm Applied to Optimizing Water Distribution Networks." Water 8, no. 5 (2016): 182. doi: 10.3390/w8050182
  • Veček, Niki, Marjan Mernik, Bogdan Filipič, and Matej Črepinšek. "Parameter tuning with Chess Rating System (CRS-Tuning) for meta-heuristic algorithms." Information Sciences 372 (2016): 446-469.
  • Yi, Lingzhi, Chengdong Zhang, and Genping Wang. "Research of Self-Tuning PID for PMSM Vector Control based on Improved KMTOA." International Journal of Intelligent Systems and Applications 9, no. 3 (2017): 60.
  • Xiao, Benxian, Jun Xiao, Rongbao Chen, and Yanhong Li. "PID Controller Parameters Tuning Based-on Satisfaction for Superheated Steam Temperature of Power Station Boiler." International Journal of Information Technology and Computer Science (IJITCS) 6, no. 7 (2014): 9.
  • Kapoor, Neha, and Jyoti Ohri. "Improved PSO tuned Classical Controllers (PID and SMC) for Robotic Manipulator." International Journal of Modern Education and Computer Science 7, no. 1 (2015): 47.
  • Deuri, Jonti, and S. Siva Sathya. "A novel cricket chirping algorithm for engineering optimization problem." Advances in Natural and Applied Sciences 9, no. 6 SE (2015): 397-403.
  • Jonti Deuri, S. S. Sathya, Cricket chirping algorithm: an ecient metaheuristic for numerical function optimization, International Journal of Computational Science and Engineering, in press.
  • Alexander, Richard D. "Aggressiveness, territoriality, and sexual behavior in field crickets (Orthoptera: Gryllidae)." Behaviour (1961): 130-223.
  • Brown, William D., Adam T. Smith, Brian Moskalik, and Josh Gabriel. "Aggressive contests in house crickets: size, motivation and the information content of aggressive songs." Animal Behaviour 72, no. 1 (2006): 225-233. doi.org/10.1016/j.anbehav.2006.01.012
  • Mays, David L. "Mating Behavior of Nemobiine Crickets: Hygronemobius, Nemobius, and Pteronemobius (Orthoptera: Gryllidae)." Florida Entomologist (1971): 113-126.DOI: 10.2307/3493557
  • Dolbear, A. E. "The cricket as a thermometer." The American Naturalist 31, no. 371 (1897): 970-971.
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