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Elephant swarm water search algorithm for global optimization

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Abstract

The rising complexity of real-life optimization problems has constantly inspired computer researchers to develop new efficient optimization methods. Evolutionary computation and metaheuristics based on swarm intelligence are very popular nature-inspired optimization techniques. In this paper, the author has proposed a novel elephant swarm water search algorithm (ESWSA) inspired by the behaviour of social elephants, to solve different optimization problems. This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought. Initially, we perform preliminary parametric sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values in real-life problems. In addition, the algorithm is evaluated against a number of widely used benchmark functions for global optimizations, and it is observed that the proposed algorithm has better performance for most of the cases compared with other state-of-the-art metaheuristics. Moreover, ESWSA performs better during fitness test, convergence test, computational complexity test, success rate test and scalability test for most of the benchmarks. Next, ESWSA is tested against two well-known constrained optimization problems, where ESWSA is found to be very efficient in term of execution speed and best fitness. As an application of ESWSA to real-life problem, it has been tested against a benchmark problem of computational biology, i.e., inference of Gene Regulatory Network based on Recurrent Neural Network. It has been observed that the proposed ESWSA is able to reach nearest to global minima and enabled inference of all true regulations of GRN correctly with less computational time compared with the other existing metaheuristics.

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References

  1. Gandomi A H, et al 2013 Metaheuristic applications in structures and infrastructures, 1st ed. Elsevier, USA

  2. Bianchi L, et al 2009 A survey on metaheuristics for stochastic combinatorial optimization. Nat. Comput.: Int. J. 8(2): 239–287

    Article  MathSciNet  MATH  Google Scholar 

  3. Mirjalili S, Mirjalili S M and Lewis 2014 A grey wolf optimizer. Adv. Eng. Softw. 69: 46–61, doi: http://dx.doi.org/10.1016/j.advengsoft.2013.12.007

  4. Kirkpatrick S, Gelatt Jr C D and Vecchi M P 1983 Optimization by simulated annealing. Science, 220: 671–680

    Article  MathSciNet  MATH  Google Scholar 

  5. Lawrence D D1991 Handbook of genetic algorithms, 1st ed. New York: Van Nostrand Reinhold

  6. Deb K 1999 An introduction to genetic algorithms. Sadhana 24: 293–315, https://doi.org/10.1007/bf02823145

    Article  MathSciNet  MATH  Google Scholar 

  7. Storn R and Price K 1997 Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11: 341–359

    Article  MathSciNet  MATH  Google Scholar 

  8. Yao X, Liu Y and Lin G 1999 Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3: 82–102

    Article  Google Scholar 

  9. Simon D 2008 Biogeography-based optimization. IEEE Trans. Evol. Comput. 12: 702–713

    Article  Google Scholar 

  10. Erol O K and Eksin I 2006 A new optimization method: big bang–big crunch. Adv. Eng. Softw. 37: 106–111

    Article  Google Scholar 

  11. Rashedi E, Pour H N and Saryazdi S 2009 GSA: a gravitational search algorithm. Inf. Sci. 179: 2232–2248

    Article  MATH  Google Scholar 

  12. Hatamlou A 2012 Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. 222: 175–184

    Article  MathSciNet  Google Scholar 

  13. Hosseini H S 2011 Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimization. Int. J. Comput. Sci. Eng. 6: 132–140

    Article  Google Scholar 

  14. Bonabeau E, Dorigo M and Theraulaz G 1999 Swarm intelligence: from natural to artificial systems. USA: Oxford University Press

    MATH  Google Scholar 

  15. Eberhart R C and Shi Y H 2000 Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceeding of the IEEE Congress on Evolutionary Computation, pp. 84–88

  16. Yang X S 2010 A new metaheuristic bat-inspired algorithm. In: Proceedings of Nature Inspired Cooperative Strategies for Optimization (NISCO 2010), vol. 284, pp. 65–74

  17. Yang X S and Deb S 2010 Engineering optimisation by cuckoo search. Int. J. Math. Model. Num. Optim. 1(4): 330–343

    MATH  Google Scholar 

  18. Yang X S 2012 Flower pollination algorithm for global optimization. Proceedings of Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, vol. 7445, pp. 240–249

    Article  MATH  Google Scholar 

  19. Yang X S 2010 Firefly algorithm, stochastic test functions and design optimization. Int. J. Bio-Inspired Comput. 2: 78–84

    Article  Google Scholar 

  20. Dorigo M, Maniezzo V and Colorni A 1996 Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1): 29–41

    Article  Google Scholar 

  21. Karaboga D and Basturk B 2007 A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3): 459–471

    Article  MathSciNet  MATH  Google Scholar 

  22. Yu J J Q and Li V O K 2015 A social spider algorithm for global optimization. Appl. Soft Comput. 30: 614–627, https://doi.org/10.1016/j.asoc.2015.02.014

    Article  Google Scholar 

  23. De A, Kumar S K, Gunasekaran A and Tiwari M K 2017 Sustainable maritime inventory routing problem with time window constraints. Eng. Appl. Artif. Intell. 61: 77–95, doi: https://doi.org/10.1016/j.engappai.2017.02.012

    Article  Google Scholar 

  24. Alexandridis A, Chondrodima E and Sarimveis H 2016 Cooperative learning for radial basis function networks using particle swarm optimization. Appl. Soft Comput. 49: 485–497, doi: https://doi.org/10.1016/j.asoc.2016.08.032

    Article  Google Scholar 

  25. De A, Mamanduru V K R, Gunasekaran A, Subramanian N and Tiwari M K 2016 Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization. Comput. Ind. Eng. 96(C): 201–215, doi: http://dx.doi.org/10.1016/j.cie.2016.04.002

  26. Soleimani H and Kannan G 2015 A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Appl. Math. Model. 39(14): 3990–4012

    Article  MathSciNet  Google Scholar 

  27. Wolpert D H and Macready W G 1997 No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1): 67–82

    Article  Google Scholar 

  28. Wilson E O 2000 Sociobiology: the new synthesis, 25th anniversary ed. Cambridge, England: The Belknap Press of Harvard University Press

    Google Scholar 

  29. https://en.wikipedia.org/wiki/Elephant#cite_noteShoshani120135 [Accessed on 26/03/2016]

  30. Shoshani J and Eisenberg J F 1982 Elephas maximus. Mammal. Species 182: 1–8 https://doi.org/10.2307/3504045, JSTOR 3504045

  31. Archie E A, Moss C J and Alberts S C 2006 The ties that bind: genetic relatedness predicts the fission and fusion of social groups in wild African elephants. Proc. R. Soc. B 273: 513–522

    Article  Google Scholar 

  32. Archie E A and Chiyo P I 2012 Elephant behavior and conservation: social relationships, the effects of poaching, and genetic tools for management. Mol. Ecol. 21: 765–778

    Article  Google Scholar 

  33. Adams R Social behavior and communication in elephants—it’s true! elephants don’t forget!. http://www.wildlifepictures-online.com/elephant-communication.html [accessed March 14th 2013]

  34. Krebs J R and Davies N B 1993 An introduction to behavioral ecology, 3rd ed. Oxford, UK: Blackwell Publishing

    Google Scholar 

  35. Plotnik J M, de Waal F B M and Reiss D 2006 Self-recognition in an Asian elephant. Proc. Natl. Acad. Sci. 103(45): 17053–17057 https://doi.org/10.1073/pnas.0608062103

    Article  Google Scholar 

  36. Rensch B 1957 The intelligence of elephants. Sci. Am. 196(2): 44–49 https://doi.org/10.1038/scientificamerican025744

    Article  Google Scholar 

  37. Wyatt T D 2003 Pheromones and animal behavior—communication by smell and taste. UK: Cambridge University Press

    Book  Google Scholar 

  38. http://www.elephantvoices.org/elephant-communication.html [Accessed on 16/06/2016]

  39. Hassan R, Cohanim B, Weck O D and Venter G 2005 A comparison of particle swarm optimization and the genetic algorithm. In: Proceedings of the 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, p. 1897

  40. Jamil M and Yang X S 2013 A literature survey of benchmark functions for global optimization problems. Int. J. Math. Model. Num. Optim. 4(2): 150–194, https://doi.org/10.1504/ijmmno.2013.055204

    MATH  Google Scholar 

  41. Nickabadi A, Ebadzadeh M M and Safabakhsh R 2011 A novel particle swarm optimization algorithm with adaptive inertia weight. Appl. Soft Comput. 11: 3658–3670, https://doi.org/10.1016/j.asoc.2011.01.037

    Article  Google Scholar 

  42. Shi Y H and Eberhart R C 1998 A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73

  43. Shi Y H and Eberhart R C 2000 Experimental study of particle swarm optimization. In: Proceedings of the SCI2000 Conference, vol. 3, pp. 1945–1950

  44. Xin J, Chen G and Hai Y 2009 A particle swarm optimizer with multistage linearly-decreasing inertia weight. In: Proceedings of the International Joint Conference on Computational Sciences and Optimization (CSO-2009), vol. 1, pp. 505–508

  45. Yang X S 2011 Bat algorithm for multiobjective optimization. Int. J. Bio-Inspired Comput. 3(5): 267–274

    Article  Google Scholar 

  46. Mandal S, Khan A, Saha G and Pal R K 2016 Reverse engineering of gene regulatory networks based on S-systems and bat algorithm. J. Bioinf. Comput. Biol. 14(3): 1–22, https://doi.org/10.1142/s0219720016500104

    Article  Google Scholar 

  47. Yang X S and Deb S 2014 Cuckoo search: recent advances and applications. Neural Comput. Appl. 24(1): 169–174

    Article  Google Scholar 

  48. Mandal S, Khan A, Saha G and Pal R K 2016 Large scale recurrent neural network based modeling of gene regulatory network using cuckoo search-flower pollination algorithm. Adv. Bioinf. 2016: 1–9, http://dx.doi.org/10.1155/2016/5283937

    Article  Google Scholar 

  49. Yang X S, Karamanoglu M and He X S 2014 Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46(9): 1222–1237

    Article  MathSciNet  Google Scholar 

  50. Khan A, Mandal S, Pal R K and Saha G 2016 Construction of gene regulatory networks using recurrent neural networks and swarm intelligence. Scientifica 2016: 1–14, https://doi.org/10.1155/2016/1060843

    Article  Google Scholar 

  51. Talaslioglu T 2013 Global stability-based design optimization of truss structures using multiple objectives. Sadhana 38(1): 37–68

    Article  MathSciNet  MATH  Google Scholar 

  52. Askarzadeh A 2016 A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169: 1–12

    Article  Google Scholar 

  53. Jong H D 2002 Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9(1): 67–103

    Article  Google Scholar 

  54. Masys D R 2001 Linking microarray data to the literature. Nat. Genet. 28: 9–10

    Google Scholar 

  55. Kolen J F and Kremer S C 2001 A field guide to dynamical recurrent networks. Wiley, USA

  56. Frank E S, Matthias D and Benjamin H K 2014 Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks. Front. Cell Dev. Biol. 2: 1–7, https://doi.org/10.3389/fcell.2014.00038

    Google Scholar 

  57. Palafox L, Noman N and Iba H 2013 Study on the use of evolutionary technique for inference in gene regulatory networks. In: Proceedings of Information and Communication Technology (PICT 6), pp. 82–92

  58. Palafox L, Noman N and Iba H 2013 Reconstruction of gene regulatory networks from gene expression data using decoupled recurrent neural network model. In: Proceedings of Information and Communication Technology (PICT 6), pp. 93–103

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  1. Department of Electronics and Communication Engineering, Global Institute of Management and Technology, Krishna Nagar, 741102, India

    S Mandal

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Mandal, S. Elephant swarm water search algorithm for global optimization. Sādhanā 43, 2 (2018). https://doi.org/10.1007/s12046-017-0780-z

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