Advertisement

Controlled showering optimization algorithm: an intelligent tool for decision making in global optimization

  • Javaid Ali
  • Muhammad Saeed
  • Muhammad Farhan Tabassam
  • Shaukat IqbalEmail author
S.I. : CMKBO
  • 26 Downloads

Abstract

In this study a novel population based meta-heuristic, called controlled showering optimization (CSO) algorithm, for global optimization of unconstrained problems is presented. Modern irrigation systems are equipped with smart tools manufactured and controlled by human intelligence. The proposed CSO algorithm is inspired from the functioning of water distribution tools to model search agents for carrying out the optimization process. CSO imitates the mechanism of projection of water units by sprinklers and the movements of their platforms to the desired locations for scheming optimum searching procedures. The proposed method has been tested using a number of diverse natured benchmark functions with low and high dimensions. Statistical analysis of the empirical data demonstrates that CSO offers solutions of better quality in comparison with several well-practiced algorithms like genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony (ABC), covariance matrix adaptation evolution strategy (CMA-ES), teaching and learning based optimization (TLBO) and water cycle algorithm (WCA). The experiments on high-dimensional problems reveal that CSO algorithm also outperforms significantly a number of algorithms designed specifically for high dimensional global optimization problems.

Keywords

Global optimization Population based meta-heuristic Sprinklers search agents Controlled showering Benchmarks 

Notes

References

  1. Ahrari A, Atai AA (2010) Grenade explosion method—a novel tool for optimization of multimodal functions. Appl Soft Comput 10:1132–1140Google Scholar
  2. Ali MZ, Salhieh A, Snanieh RTA, Reynolds RG (2012) Boosting cultural algorithms with a heterogeneous layered social fabric influence function. J Comput Math Org Theor 18:193–210Google Scholar
  3. Ali J, Saeed M, Chaudhry NA, Luqman M, Tabassum MF (2015) Artificial showering algorithm: a new meta-heuristic for unconstrained optimization. Sci Int (Lahore) 27(6):4939–4942Google Scholar
  4. Alihodzic A, Tuba M (2014) Improved bat algorithm applied to multilevel image thresholding. Sci World J 176718:16.  https://doi.org/10.1155/2014/176718 Google Scholar
  5. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of IEEE Congress Evolutionary Computation, Singapore, pp. 4661–4667Google Scholar
  6. Brajevic I, Tuba M (2014) Cuckoo search and firefly algorithm applied to multilevel image thresholding in Cuckoo Search and Firefly Algorithm: theory and applications. Springer Int Publ 516:115–139Google Scholar
  7. Brest J, Zamuda A, Boskovic B, Maucec MS, Zumer V (2008) High-dimensional real-parameter optimization using self-adaptive differential evolution algorithm with population size reduction. In Proc IEEE Congr Evol Comput 2032–2039Google Scholar
  8. Coope ID, Price CJ (2000) Frame Based Methods for Unconstrained Optimization. J Optimiz Theory App 107:261–274Google Scholar
  9. Coope ID, Price CJ (2001) On the convergence of grid-based methods for unconstrained optimization. SIAM J Optim 11:859–869Google Scholar
  10. Corporation RB (2018) 29JH Impact Sprinkler, http://www.rainbird.com/ag/products/impacts/29JH.htm
  11. Davis C (1954) Theory of positive linear dependence. AM J Math 76:733–746Google Scholar
  12. Derrac J, Garcia S, Hui S, Suganthan PN, Herrera F (2014) Analyzing convergence performance of evolutionary algorithms: a statistical approach. Inf Sci 289:41–58Google Scholar
  13. Dog˘an B, Ölmez T (2015) A new metaheuristic for numerical function optimization: Vortex Search algorithm. Inf Sci 293:125–145Google Scholar
  14. DUCAR (2017) Irricruiser ultimate travelling irrigator http://www.irrigationbox.com.au
  15. Dymond AS, Engelbrecht AP, Kok S, Heyns PS (2015) Tuning optimization algorithms under multiple objective function evaluation budgets. IEEE Trans Evolut Comput 19(3):341–358Google Scholar
  16. Engelbrecht AP (2014) Fitness function evaluations: A fair stopping condition? In Proceedings of the IEEE Swarm Intelligence Symposium 1-8Google Scholar
  17. Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water Cycle Algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166Google Scholar
  18. Formato RA (2011) Central Force Optimization with variable initial probes and adaptive decision space. Appl Math Comput 217:8866–8872Google Scholar
  19. García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180(10):2044–2064Google Scholar
  20. Garg H (2016) A hybrid PSO-GA algorithm for constrained optimization problems. Appl Math Comput 274:292–305Google Scholar
  21. Ghaheri A, Shoar S, Naderan M, Hoseini SS (2015) The applications of genetic algorithms in medicine. Oman Med J 30(6):406–416.  https://doi.org/10.5001/omj.2015.82 Google Scholar
  22. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Pearson publishers, IndiaGoogle Scholar
  23. Hajihassani M, Armaghani J, Kalatehjari D (2017) Applications of particle swarm optimization in geotechnical engineering: a comprehensive review. Geol Eng, Geotech.  https://doi.org/10.1007/s10706-017-0356-z Google Scholar
  24. Hakli H, Uguz H (2014) A novel particle swarm optimization with levy flight. Appl Soft Comput 23:333–345Google Scholar
  25. Hansen N, Auger A, Mersmann O, Tušar T, Brockhoff D (2016) COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting. ArXiv e-prints, arXiv:1603.08785
  26. Hieu TTA (2011) Water Flow Algorithm for Optimization Problems. PhD thesis, National University of SingaporeGoogle Scholar
  27. Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  28. Hosseini HS (2007) Problem Solving By Intelligent Water Drops. In: Proceedings of IEEE Congress Evolutionary Computation. pp 3226–3231Google Scholar
  29. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471Google Scholar
  30. Kaveh A (2017a) Applications of metaheuristic optimization algorithms in civil engineering. Springer, SwitzerlandGoogle Scholar
  31. Kaveh A (2017b) Advances in metaheuristic algorithms for optimal design of structures. Springer, SwitzerlandGoogle Scholar
  32. Kennedy J, Eberhart R (1995) Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp 1942–1948Google Scholar
  33. Kim IK, Jung DW, Park RH (2002) Document Image Binarization Based on Topographic Analysis Using a Water Flow Model. Pattern Recog 35(1):265–277Google Scholar
  34. Kirkpatrick S, Gellat CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680Google Scholar
  35. Li X, Yao X (2012) Cooperatively coevolving particle swarms for large scale optimization. IEEE T. Evolut Comput 16(2):210–224Google Scholar
  36. Li X, Engelbrecht A, Epitropakis M (2013) Benchmark Functions for CEC 2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization. Tech Rep School of Computer Science and Information Technology RMIT University Melbourne AustraliaGoogle Scholar
  37. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive Learning particle swarm optimization for global optimization of multimodal functions. IEEE Trans Evol Comput 10:281–295Google Scholar
  38. Liang JJ, Qu BY, Suganthan P, Hern´andez-D´ıaz A (2013) Problem definitions and evaluation criteria for the CEC 2013 special session and competition on real-parameter optimization. Tech Rep Computational Intelligence Laboratory Zhengzhou University Zhengzhou, ChinaGoogle Scholar
  39. Liang JJ, Qu BY, Suganthan PN (2014) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Tech Rep 201311 Computational Intelligence Laboratory Zhengzhou University, Zhengzhou, ChinaGoogle Scholar
  40. Majumdar DK (2010) Irrigation water management: principles and practice. New Delhi PHI learning Pvt LtdGoogle Scholar
  41. Mariani VC, Luvizotto LGJ, Guerra FA, Coelho LDS (2011) A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization. Appl Math Comput 217:5822–5829Google Scholar
  42. Meng KO, Pauline O, Kiong SC, Wahab HA, Jafferi N (2017) Application of modified flower pollination algorithm on mechanical engineering design problem. IOP Conference Series 165:012032Google Scholar
  43. Omidvar MN, Li X (2011) A comparative study of CMA-ES on large scale global optimization. Advances in artificial intelligence. Springer, New York, pp 303–312Google Scholar
  44. Ostermeier HN (2001) A Completely de-randomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195Google Scholar
  45. Ponsich A, Jaimes AL, Coello CAC (2013) A survey on multi-objective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Trans Evol Comput 17(3):321–344Google Scholar
  46. Price CJ, Coope ID (2003) Frame-based ray search algorithm in un-constrained optimization. J Optimiz Theor App 116(2):359–377Google Scholar
  47. Rao RV, Savsani VJ, Vakharia DP (2001) Teaching–learning-based-optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):257–268Google Scholar
  48. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a Gravitational Search Algorithm. Inform Sciences 179:2232–2248Google Scholar
  49. Reynolds RG (1994) An introduction to cultural algorithms. Proc Ann Conf Evolut Comput World Sci 11(3):294–307Google Scholar
  50. Saad AH, Dong Z, Karimi M (2017) A Comparative study on recently-introduced nature-based global optimization methods in complex mechanical system design. Algorithms 10(4):120.  https://doi.org/10.3390/a10040120 Google Scholar
  51. Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine Blast Algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13:2592–2612Google Scholar
  52. Sadollah A, Eskander H, Bahreinejad A, Kim JH (2015) Water Cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems. Appl Soft Comput 30:58–71Google Scholar
  53. Shang YW, Qiu YH (2006) A note on extended Rosenbrock function. Evolut Comput 14:119–126Google Scholar
  54. Srinivasan D, Seow TH (2003) Evolutionary Computation (CEC’03). Congr Evol Comput 4:2292–2297Google Scholar
  55. Storn R, Price K (1997) Differential evolution- a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359Google Scholar
  56. Suganthan P, Hansen N, Liang J, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Tech Rep Nanyang Technological UniversityGoogle Scholar
  57. Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005b) Problem definitions and evaluation criteria for the cec 2005 special session on real parameter optimization. Technical report. Nanyang Technological University, SingaporeGoogle Scholar
  58. Sun J, Garibaldi JM, Hodgman C (2012) Parameter estimation using metaheuristics in systems biology: a comprehensive review. IEEE/ACM Trans Comput Biol Bioinform 9(1):185–202Google Scholar
  59. Tang K, Yao X, Suganthan PN, MacNish C, Chen YP, Chen CM, Yang Z (2008) Benchmark functions for the CEC’2008 special session and competition on large scale global optimization, Nature Inspired Computation and Applications Laboratory, USTC. Applicat Lab Univ Sci Technol ChinaGoogle Scholar
  60. Tang K, Li X, Suganthan P, Yan Z, Wiese T (2010) Benchmark Functions for the CEC 2010 Special Session and Competition on Large-Scale Global Optimization. Tech Rep School of Computer Science and Technology, University of Science and Technology of ChinaGoogle Scholar
  61. Tseng LY, Chen C (2008) Multiple Trajectory Search for Large Scale Global Optimization. In: Proceedings of the IEEE Conference on Evolutionary Computation. pp 3052–3059Google Scholar
  62. Wang Y, Li B (2008) A restart univariate estimation of distribution algorithm sampling under mixed Gaussian and Lévy probability distribution. Proc Congr Evol Comput.  https://doi.org/10.1109/CEC.2008.4631330 Google Scholar
  63. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82Google Scholar
  64. Yang XS (2012) Free lunch or no free lunch: that is not just a question? Int J Artif Intell T 21(3):5360–5366Google Scholar
  65. Yang FC, Wang YP (2007) Water flow-like algorithm for object grouping problems. J Chin Inst Ind Eng 24(6):475–488Google Scholar
  66. Yang Z, Tang K, Yao X (2008) Multilevel cooperative coevolution for large scale optimization. In: Proceedings of IEEE World Congress on Computational Intelligence. pp 1663–1670Google Scholar
  67. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102.  https://doi.org/10.1109/4235.771163 Google Scholar
  68. Zhang L, Liu L, Yang XS, Dai Y (2016) A novel hybrid firefly algorithm for global optimization. PLoS ONE 11(9):e0163230.  https://doi.org/10.1371/journal.pone.0163230 Google Scholar
  69. Zhao S, Liang J, Suganthan P (2008) Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization. In: Proceedings of IEEE CEC pp 3845–3852Google Scholar
  70. Zheng YJ (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Javaid Ali
    • 1
  • Muhammad Saeed
    • 1
  • Muhammad Farhan Tabassam
    • 1
  • Shaukat Iqbal
    • 2
    Email author
  1. 1.Department of Mathematics, School of SciencesUniversity of Management and TechnologyLahorePakistan
  2. 2.Department of Informatics, School of Systems and TechnologyUniversity of Management and TechnologyLahorePakistan

Personalised recommendations