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Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

  • Yazdani, Maziar (School of Industrial Engineering, College of Engineering, University of Tehran) ;
  • Jolai, Fariborz (School of Industrial Engineering, College of Engineering, University of Tehran)
  • Received : 2015.03.10
  • Accepted : 2015.06.02
  • Published : 2016.01.01

Abstract

During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.

Keywords

References

  1. Sundar S, Singh A. A swarm intelligence approach to the early/tardy scheduling problem. Swarm Evolut. Comput. 2012;4(0)25-32. https://doi.org/10.1016/j.swevo.2011.12.002
  2. Suresh K, Kumarappan N. Hybrid improved binary particle swarm optimization approach for generation maintenance scheduling problem. Swarm Evolut. Comput. 2013;9(0)69-89. https://doi.org/10.1016/j.swevo.2012.11.003
  3. Layegh J, Jolai F. A memetic algorithm for minimizing the total weighted completion time on a single machine under linear deterioration. Appl. Math. Model. 2010;34(10)2910-25. https://doi.org/10.1016/j.apm.2010.01.002
  4. Soltani R, Jolai F, Zandieh M. Two robust meta-heuristics for scheduling multiple job classes on a single machine with multiple criteria. Expert Syst. Appl. 2010;37(8)5951-9. https://doi.org/10.1016/j.eswa.2010.02.009
  5. Behnamian, J, et al. Minimizing makespan on a three-machine flowshop batch scheduling problem with transportation using genetic algorithm. Appl. Soft Comput 2012;12(2)768-77. https://doi.org/10.1016/j.asoc.2011.10.015
  6. Goldansaz SM, Jolai F, Zahedi Anaraki AH. A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop. Appl. Math. Model. 2013;37(23)9603-16. https://doi.org/10.1016/j.apm.2013.05.002
  7. Senthilnath J, Omkar SN, Mani V. Clustering using firefly algorithm: performance study. Swarm Evolut. Comput. 2011;1(3)164-71. https://doi.org/10.1016/j.swevo.2011.06.003
  8. Nanda SJ, Panda G. A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evolut. Comput. 2014;16(0)1-18. https://doi.org/10.1016/j.swevo.2013.11.003
  9. Panda R, Naik MK, Panigrahi BK. Face recognition using bacterial foraging strategy. Swarm Evolut. Comput. 2011;1(3)138-46. https://doi.org/10.1016/j.swevo.2011.06.001
  10. Fornarelli G, Giaquinto A. An unsupervised multi-swarm clustering technique for image segmentation. Swarm Evolut. Comput. 2013;11(0)31-45. https://doi.org/10.1016/j.swevo.2013.02.002
  11. Saraswat M, Arya KV, Sharma H. Leukocyte segmentation in tissue images using differential evolution algorithm. Swarm Evolut. Comput. 2013;11(0)46-54. https://doi.org/10.1016/j.swevo.2013.02.003
  12. Draa A, Bouaziz A. An artificial bee colony algorithm for image contrast enhancement. Swarm Evolut. Comput. 2014;16(0)69-84. https://doi.org/10.1016/j.swevo.2014.01.003
  13. Malviya R, Pratihar DK. Tuning of neural networks using particle swarm optimization to model MIG welding process. Swarm Evolut. Comput. 2011;1(4)223-35. https://doi.org/10.1016/j.swevo.2011.07.001
  14. Azadeh A, Seif J, Sheikhalishahi M, Yazdani M. An integrated support vector regression-imperialist competitive algorithm for reliability estima-tion of a shearing machine. Int. J. Comput. Integr. Manuf. 2015: 1-9http://dx.doi.org/10.1080/0951192X.2014.1002810.
  15. Meysam Mousavi, S, et al. A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects. Robot. Computer Integr. Manuf. 2013;29(1)157-68. https://doi.org/10.1016/j.rcim.2012.04.006
  16. Liu H-C, Huang J-S. Pattern recognition using evolution algorithms with fast simulated annealing. Pattern Recognit. Lett. 1998;19(5-6)403-13. https://doi.org/10.1016/S0167-8655(98)00025-7
  17. Suganthan PN. Structural pattern recognition using genetic algorithms. Pattern Recognit. 2002;35(9)1883-93. https://doi.org/10.1016/S0031-3203(01)00136-4
  18. Garai G, Chaudhurii BB. A novel hybrid genetic algorithm with Tabu search for optimizing multi-dimensional functions and point pattern recognition. Inf. Sci. 2013;221(0)28-48. https://doi.org/10.1016/j.ins.2012.09.012
  19. Oftadeh R, Mahjoob MJ, Shariatpanahi M. A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput. Math. Appl. 2010;60(7)2087-98. https://doi.org/10.1016/j.camwa.2010.07.049
  20. Bhargava V, Fateen SEK, Bonilla-Petriciolet A. Cuckoo search: a new nature-inspired optimization method for phase equilibrium calculations. Fluid Phase Equilib. 2013;337(0)191-200. https://doi.org/10.1016/j.fluid.2012.09.018
  21. Zheng Y-J. Water wave optimization: a new nature-inspired metaheur-istic. Comput. Oper. Res. 2015;55(0)1-11.
  22. Holland JH. Adaptation in Natural and Artificial Systems: An Introduc-tory Analysis with Applications to Biology, Control, and Artificial Intelligence. U Michigan Press; 1975.
  23. Farmer JD, Packard NH, Perelson AS. The immune system, adaptation, and machine learning. Physica D: Nonlinear Phenom. 1986;22(1)187-204. https://doi.org/10.1016/0167-2789(86)90240-X
  24. Dorigo M. Optimization, learning and natural algorithms Ph.D. thesis. Italy: Politecnico di Milano; 1992.
  25. R.C., Eberhart and J. Kennedy, A new optimizer using particle swarm theory, in: Proceedings of the sixth International Symposium on Micro Machine and Human Science, New York, NY, 1995.
  26. H.A., Abbass, MBO: marriage in honey bees optimization-a haplome-trosis polygynous swarming approach, in: Proceedings of the IEEE Congress on Evolutionary Computation, 2001.
  27. Passino KM. Biomimicry of bacterial foraging for distributed optimiza-tion and control. Control Syst. IEEE 2002;22(3)52-67. https://doi.org/10.1109/MCS.2002.1004010
  28. Eusuff MM, Lansey KE. Optimization of water distribution network design using the shufed frog leaping algorithm. J. Water Res. Plan. Manag. 2003;129(3)210-25. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:3(210)
  29. Chu S-C, Tsai P-W, Pan J-S. Cat swarm optimization. PRICAI 2006: Trends in Artificial Intelligence. Springer; 854-8.
  30. Mehrabian AR, Lucas C. A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 2006;1(4)355-66. https://doi.org/10.1016/j.ecoinf.2006.07.003
  31. Mucherino A, Seref O. Monkey search: a novel metaheuristic search for global optimization. Data Mining, Systems Analysis and Optimization in Biomedicine. AIP Publishing; 2007.
  32. Yang F-C, Wang Y-P. Water flow-like algorithm for object grouping problems. J. Chin. Inst. Ind. Eng. 2007;24(6)475-88. https://doi.org/10.1080/10170660709509062
  33. Simon D. Biogeography-based optimization. Evolut. Comput. IEEE Trans. 2008;12(6)702-13. https://doi.org/10.1109/TEVC.2008.919004
  34. F.,de Lima Neto, et al., A novel search algorithm based on flsh school behavior, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, SMC, 2008.
  35. X.-S., Yang and S. Deb., Cuckoo Search via Levy flights, in: Proceedings of the IEEE World Congress on Nature & Biologically Inspired Computing, NaBIC, 2009. .
  36. Rajabioun R. Cuckoo optimization algorithm. Appl. Soft Comput. 2011;11(8)5508-18. https://doi.org/10.1016/j.asoc.2011.05.008
  37. Yang X-S. A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Springer; 65-74.
  38. Yang X-S. Firefly algorithms for multimodal optimization. Stochastic Algorithms: Foundations and Applications. Springer; 169-78.
  39. Y., Shiqin, J. Jianjun, and Y. Guangxing. A dolphin partner optimization. in: Proceedings of the IEEE WRI Global Congress on Intelligent Systems, GCIS, 2009.
  40. Kaveh A, Farhoudi N. A new optimization method: dolphin echolocation. Adv. Eng. Softw. 2013;59(0)53-70. https://doi.org/10.1016/j.advengsoft.2013.03.004
  41. Yang X-S. Flower pollination algorithm for global optimization. Unconventional Computation and Natural Computation. Springer; 240-9.
  42. Gandomi AH, Alavi AH. Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 2012;17(12)4831-45. https://doi.org/10.1016/j.cnsns.2012.05.010
  43. R., Tang, et al., Wolf search algorithm with ephemeral memory, in: Proceedings of the Seventh International Conference on IEEE Digital Information Management (ICDIM), 2012.
  44. Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv. Eng. Softw. 2014;69(0)46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
  45. Eskandar, H, et al. Water cycle algorithm - a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput. Struct. 2012;110-111(0)151-66. https://doi.org/10.1016/j.compstruc.2012.07.010
  46. Cuevas, E, et al. A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst. Appl. 2013;40(16)6374-84. https://doi.org/10.1016/j.eswa.2013.05.041
  47. Ghaemi M, Feizi-Derakhshi M-R. Forest optimization algorithm. Expert Syst. Appl. 2014;41(15)6676-87. https://doi.org/10.1016/j.eswa.2014.05.009
  48. Arivudainambi D, Rekha D. Memetic algorithm for minimum energy broadcast problem in wireless ad hoc networks. Swarm Evolut. Comput. 2013;12(0)57-64. https://doi.org/10.1016/j.swevo.2013.04.001
  49. Hofmann J, Limmer S, Fey D. Performance investigations of genetic algorithms on graphics cards. Swarm Evolut. Comput. 2013;12(0)33-47. https://doi.org/10.1016/j.swevo.2013.04.003
  50. Ludwig SA. Memetic algorithms applied to the optimization of workflow compositions. Swarm Evolut. Comput. 2013;10(0)31-40. https://doi.org/10.1016/j.swevo.2012.12.001
  51. Changdar C, Mahapatra GS, Kumar Pal R. An efficient genetic algorithm for multi-objective solid travelling salesman problem under fuzziness. Swarm Evolut. Comput. 2014;15(0)27-37. https://doi.org/10.1016/j.swevo.2013.11.001
  52. Wolpert DH, Macready WG. No free lunch theorems for optimization. Evolut. Comput. IEEE Trans. 1997;1(1)67-82. https://doi.org/10.1109/4235.585893
  53. Wang B, Jin X, Cheng B. Lion pride optimizer: an optimization algorithm inspired by lion pride behavior. Sci. China Inf. Sci. 2012;55(10)2369-89. https://doi.org/10.1007/s11432-012-4548-0
  54. Rajakumar B. The Lion's Algorithm: a new nature-inspired search algorithm. Procedia Technol. 2012;6:126-35. https://doi.org/10.1016/j.protcy.2012.10.016
  55. Mccomb, K, et al. Female lions can identify potentially infanticidal males from their roars. Proc. R. Soc. Lond. Ser B: Biol. Sci. 1993;252(1333)59-64. https://doi.org/10.1098/rspb.1993.0046
  56. Schaller GB. The Serengeti lion: a study of predator-prey relations. Wildlife behavior and ecology series. Chicago, Illinois, USA: University of Chicago Press; 1972.
  57. Scheel D, Packer C. Group hunting behaviour of lions: a search for cooperation. Anim. Behav. 1991;41(4)697-709. https://doi.org/10.1016/S0003-3472(05)80907-8
  58. Wilkins J. How Many Species Concepts are tHERE. London: The Guardian; 2010.
  59. S.B., Hrdy, 7 Empathy, polyandry, and the myth of the coy female, Conceptual Issues in Evolutionary Biology, 2006: p. 131.
  60. Stander PE. Cooperative hunting in lions: the role of the individual. Behav. Ecol. Sociobiol. 1992;29(6)445-54. https://doi.org/10.1007/BF00170175
  61. H.R., Tizhoosh, Opposition-based learning: a new scheme for machine intelligence, in: Proceedings of the CIMCA/IAWTIC, 2005.
  62. J., Liang, B. Qu, and P. Suganthan, Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization, Computational Intelli-gence Laboratory, 2013.
  63. Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Inf. Sci. 2009;179(13)2232-48. https://doi.org/10.1016/j.ins.2009.03.004
  64. Oftadeh R, Mahjoob M, Shariatpanahi M. A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput. Math. Appl. 2010;60(7)2087-98. https://doi.org/10.1016/j.camwa.2010.07.049
  65. Zheng Y-J. Water wave optimization: a new nature-inspired metaheur-istic. Comput. Oper. Res. 2014;55:1-11.
  66. Goldansaz SM, Jolai F, Anaraki AHZ. A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop. Appl. Math. Model. 2013;37(23)9603-16. https://doi.org/10.1016/j.apm.2013.05.002

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