Table of Contents Author Guidelines Submit a Manuscript
Journal of Optimization
Volume 2017 (2017), Article ID 3082024, 13 pages
https://doi.org/10.1155/2017/3082024
Research Article

A Metaheuristic Algorithm Based on Chemotherapy Science: CSA

Department of Industrial Engineering, Sharif University of Technology, Tehran 11365/8639, Iran

Correspondence should be addressed to Mohammad Hassan Salmani; moc.liamg@inamlashm

Received 3 June 2016; Revised 29 November 2016; Accepted 15 January 2017; Published 23 February 2017

Academic Editor: Bijaya Ketan Panigrahi

Copyright © 2017 Mohammad Hassan Salmani and Kourosh Eshghi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. Neapolitan and K. Naimipour, Foundations of Algorithms Using C++ Pseudo Code, Jones & Bartlett Learning, Burlington, Mass, USA, 3rd edition, 2004.
  2. S. Binitha and S. S. Sathya, “A survey of bio inspired optimization algorithms,” International Journal of Soft Computing and Engineering, vol. 2, no. 2, pp. 137–151, 2012. View at Google Scholar
  3. American Cancer Society, Chemotherapy What It Is, How It Helps, A.C. Society, Atlanta, Ga, USA, 2013.
  4. J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Mich, USA, 1975.
  5. F. Glover, “Future paths for integer programming and links to artificial intelligence,” Computers & Operations Research, vol. 13, no. 5, pp. 533–549, 1986. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. F. Glover and C. McMillan, “The general employee scheduling problem: an integration of MS and AI,” Computers and Operations Research, vol. 13, pp. 563–573, 1986. View at Google Scholar
  7. S. Kirkpatrick, J. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, 1983. View at Publisher · View at Google Scholar · View at MathSciNet
  8. M. Dorigo, Optimization, Learning and Natural Algorithms, Politecnico di Milano, Milan, Italy, 1992.
  9. J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Perth, Australia, 1995.
  10. Z. Beheshti and S. M. H. Shamsuddin, “A review of population-based meta-heuristic algorithm,” International Journal of Advances in Soft Computing and Its Applications, vol. 5, no. 1, pp. 1–35, 2013. View at Google Scholar · View at Scopus
  11. F. Glover, “Heuristics for integer programming using surrogate constraints,” Decision Sciences, vol. 8, no. 1, pp. 156–166, 1977. View at Publisher · View at Google Scholar
  12. S. F. Smith, A Learning System Based on Genetic Adaptive Algorithms, University of Pittsburgh, 1980.
  13. J. D. Farmer, N. H. Packard, and A. S. Perelson, “The immune system, adaptation, and machine learning,” Physica D: Nonlinear Phenomena, vol. 22, no. 1–3, pp. 187–204, 1986. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, Mass, USA, 1988.
  15. I. Axcelis, “Evolver, the world's first commercial GA product for desktop computers,” The New York Times, 1989. View at Google Scholar
  16. P. Moscato, “On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms,” Caltech Concurrent Computation Program, Technical Report C3P 826, 1989. View at Google Scholar
  17. C. M. Fonseca and P. J. Fleming, “Genetic algorithms for multiobjective optimization: formulation, discussion and generalization,” in Proceedings of the 5th International Conference on Genetic Algorithms, pp. 416–423, Urbana-Champaign, Ill, USA, 1993.
  18. R. Battiti and G. Tecchiolli, “The reactive tabu search,” ORSA Journal on Computing, vol. 6, no. 2, pp. 126–140, 1994. View at Publisher · View at Google Scholar
  19. R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Publisher · View at Google Scholar · View at MathSciNet
  20. R. Y. Rubinstein, “Optimization of computer simulation models with rare events,” European Journal of Operational Research, vol. 99, no. 1, pp. 89–112, 1997. View at Publisher · View at Google Scholar · View at Scopus
  21. E. Taillard and S. Voss, “POPMUSIC: partial optimization metaheuristic under special intensification conditions,” Tech. Rep., Institute for Computer Sciences, heig-vd, Yverdon-les-Bains, Switzerland, 1999. View at Google Scholar
  22. Z. W. Geem, J. H. Kim, and G. V. Loganathan, “A new heuristic optimization algorithm: harmony search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001. View at Publisher · View at Google Scholar · View at Scopus
  23. O. Hanseth and M. Aanestad, “Bootstrapping networks, communities and infrastructures. On the evolution of ICT solutions in heath care,” in Proceedings of the 1st International Conference on Information Technology in Health Care (ITHC '01), Erasmus University, Rotterdam, The Netherlands, 2001.
  24. S. Nakrani and C. Tovey, “On honey bees and dynamic server allocation in internet hosting centers,” Adaptive Behavior, vol. 12, no. 3-4, pp. 223–240, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. K. N. Krishnanand and D. Ghose, “Detection of multiple source locations using a glowworm metaphor with applications to collective robotics,” in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '05), pp. 84–91, IEEE, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. D. Karaboga, “An idea based on honey bee swarm for numerical numerical optimization,” Tech. Rep. TR06, Computer Engineering Department, Engineering Faculty, Erciyes University, 2005. View at Google Scholar
  27. O. B. Haddad, A. Afshar, and M. A. Mariño, “Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization,” Water Resources Management, vol. 20, no. 5, pp. 661–680, 2006. View at Publisher · View at Google Scholar · View at Scopus
  28. H. Shah-Hosseini, “Problem solving by intelligent water drops,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '07), pp. 3226–3231, Singapore, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. E. Atashpaz-Gargari and C. Lucas, “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '07), pp. 4661–4667, Singapore, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Mucherino and O. Seref, “Monkey search: a novel metaheuristic search for global optimization,” in Proceedings of the AIP Conference Proceedings, Data Mining, Systems Analysis and Optimization in Biomedicine, vol. 953, pp. 162–173, Gainesville, Fla, USA, March 2007.
  31. X.-S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2008.
  32. A. Husseinzadeh Kashan, “League Championship Algorithm: a new algorithm for numerical function optimization,” in Proceedings of the International Conference on Soft Computing and Pattern Recognition (SoCPaR '09), pp. 43–48, Malacca, Malaysia, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA: a gravitational search algorithm,” Information Sciences, vol. 179, no. 13, pp. 2232–2248, 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. X.-S. Yang and S. Deb, “Cuckoo search via Lévy flights,” in Proceedings of the World Congress on Nature and Biologically Inspired Computing (NABIC '09), pp. 210–214, Coimbatore, India, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  35. X.-S. Yang, “A new metaheuristic bat-inspired algorithm,” in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), J. R. Gonzalez, D. A. Pelta, C. Cruz, G. Terrazas, and N. Krasnogor, Eds., vol. 284 of Studies in Computational Intelligence, pp. 65–74, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  36. H. Shah-Hosseini, “Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation,” International Journal of Computational Science and Engineering, vol. 6, no. 1-2, pp. 132–140, 2011. View at Publisher · View at Google Scholar · View at Scopus
  37. K. Tamura and K. Yasuda, “Spiral dynamics inspired optimization,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 15, no. 8, pp. 1116–1122, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems,” Computer-Aided Design, vol. 43, no. 3, pp. 303–315, 2011. View at Publisher · View at Google Scholar · View at Scopus
  39. A. H. Gandomi and A. H. Alavi, “Krill herd: a new bio-inspired optimization algorithm,” Communications in Nonlinear Science and Numerical Simulation, vol. 17, no. 12, pp. 4831–4845, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  40. P. Civicioglu, “Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm,” Computers & Geosciences, vol. 46, pp. 229–247, 2012. View at Publisher · View at Google Scholar · View at Scopus
  41. A. H. Gandomi, X.-S. Yang, and A. H. Alavi, “Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems,” Engineering with Computers, vol. 29, no. 1, pp. 17–35, 2013. View at Publisher · View at Google Scholar · View at Scopus
  42. A. H. Gandomi, X.-S. Yang, S. Talatahari, and A. H. Alavi, “Firefly algorithm with chaos,” Communications in Nonlinear Science and Numerical Simulation, vol. 18, no. 1, pp. 89–98, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  43. A. Kaveh and V. R. Mahdavi, “Colliding bodies optimization: a novel meta-heuristic method,” Computers & Structures, vol. 139, pp. 18–27, 2014. View at Publisher · View at Google Scholar · View at Scopus
  44. Z. Beheshti and S. M. Shamsuddin, “CAPSO: centripetal accelerated particle swarm optimization,” Information Sciences, vol. 258, pp. 54–79, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  45. A.-B. Meng, Y.-C. Chen, H. Yin, and S.-Z. Chen, “Crisscross optimization algorithm and its application,” Knowledge-Based Systems, vol. 67, pp. 218–229, 2014. View at Publisher · View at Google Scholar · View at Scopus
  46. B. Javidy, A. Hatamlou, and S. Mirjalili, “Ions motion algorithm for solving optimization problems,” Applied Soft Computing Journal, vol. 32, pp. 72–79, 2015. View at Publisher · View at Google Scholar · View at Scopus
  47. J. J. Q. Yu and V. O. K. Li, “A social spider algorithm for global optimization,” Applied Soft Computing, vol. 30, pp. 614–627, 2015. View at Publisher · View at Google Scholar · View at Scopus
  48. R. V. Rao, “Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems,” International Journal of Industrial Engineering Computations, vol. 7, no. 1, pp. 19–34, 2016. View at Publisher · View at Google Scholar · View at Scopus
  49. M. H. Salmani and K. Eshghi, “A Smart Structural Algorithm (SSA) based on infeasible region to solve mixed integer problems,” International Journal of Applied Metaheuristic Computing, vol. 8, pp. 24–44, 2017. View at Google Scholar
  50. AIULd Bruxelles, TSPTW-Benchmark Problems, Université libre de Bruxelles, Brussels, Belgium, 2006.
  51. G. Pataki, The Bad and the Good-and-Ugly: Formulations for the Traveling Salesman Problem, Department of Industrial Engineering and Operation Research, Columbia University, 2001.
  52. I. GitHub, “ViktorCollin lagt till test cases,” in TSP Problem, San Francisco, Millions of developers use GitHub to build personal projects, support their businesses, and work together on open source technologies, 2012. View at Google Scholar