Skip to main content

Advertisement

SpringerLink
Log in

Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

This paper proposes a novel nature-inspired algorithm called Gaining Sharing Knowledge based Algorithm (GSK) for solving optimization problems over continuous space. The GSK algorithm mimics the process of gaining and sharing knowledge during the human life span. It is based on two vital stages, junior gaining and sharing phase and senior gaining and sharing phase. The present work mathematically models these two phases to achieve the process of optimization. In order to verify and analyze the performance of GSK, numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions. Besides, the GSK algorithm has been applied to solve the set of real world optimization problems proposed for the IEEE-CEC2011 evolutionary algorithm competition. A comparison with 10 state-of-the-art and recent metaheuristic algorithms are executed. Experimental results indicate that in terms of robustness, convergence and quality of the solution obtained, GSK is significantly better than, or at least comparable to state-of-the-art approaches with outstanding performance in solving optimization problems especially with high dimensions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Talbi E-G (2009) Metaheuristics : from design to implementation. Wiley, New York

    MATH  Google Scholar 

  2. Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13(5):533–549

    MathSciNet  MATH  Google Scholar 

  3. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization. ACM Comput Surv 35(3):268–308

    Google Scholar 

  4. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley

  5. Rechenberg I (1994) Evolutionsstrategie: optimierung technischer systeme nach prinzipien der biologischen evolution. Frommann-holzbog, Stuttgart, 1973

  6. Holland J (1975) Adaptation in natural and artificial systems : an introductory analysis with application to biology. Control and artificial intelligence, University of Michigan Press. https://ci.nii.ac.jp/naid/10019844035/en/

  7. Hillis WD (1990) Co-evolving parasites improve simulated evolution as an optimization procedure. Phys D Nonlinear Phenom 42(1–3):228–234

    Google Scholar 

  8. Reynolds RG (1994) An introduction to cultural algorithms. In: Proceedings of the 3rd annual conference on evolutionary programming. World Scienfific Publishing, pp 131–139

  9. Koza J (1994) Genetic programming as a means for programming computers by natural selection. Stat Comput 4(2):87–112

    Google Scholar 

  10. Mühlenbein H, Paaß G (1996) From recombination of genes to the estimation of distributions I. Binary parameters. Springer, Berlin, pp 178–187

    Google Scholar 

  11. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    MathSciNet  MATH  Google Scholar 

  12. Ryan C, Collins J, Neill MO (1998) Grammatical evolution: evolving programs for an arbitrary language. Springer, Berlin, pp 83–96

    Google Scholar 

  13. Ferreira C (2002) Gene expression programming in problem solving. In: Soft computing and industry. Springer London, pp 635–653

  14. Han K-H, Kim J-H (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans Evol Comput 6(6):580–593

    MathSciNet  Google Scholar 

  15. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Congr Evol Comput 2007:4661–4667

    Google Scholar 

  16. Civicioglu P (2012) Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci 46:229–247

    Google Scholar 

  17. Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144

    MathSciNet  MATH  Google Scholar 

  18. Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl Based Syst 75:1–18

    Google Scholar 

  19. Dhivyaprabha TT, Subashini P, Krishnaveni M (2018) Synergistic fibroblast optimization: a novel nature-inspired computing algorithm. Front Inf Technol Electron Eng 19(7):815–833

    Google Scholar 

  20. Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts-towards memetic algorithms

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

    Google Scholar 

  22. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the sixth international symposium on micro machine and human science, pp 39–43

  23. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, IV, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  24. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: 1997 IEEE international conference on systems, man, and cybernetics. Computational cybernetics and simulation, vol 5, pp 4104–4108

  25. de Castro LN, Timmis J (2002) Artificial immune systems: a new computational approach. Springer-Verlag, London, UK

    MATH  Google Scholar 

  26. de Castro LN, Von Zuben FJ (1999) Artificial immune systems: part I -basic theory and applications. School of Computing and Electrical Engineering, State University of Campinas, Brazil, No. DCA-RT 01/99

  27. Zelinka I (2004) SOMA—self-organizing migrating algorithm. Springer, Berlin, pp 167–217

    MATH  Google Scholar 

  28. Abbass HA (2001) MBO: marriage in honey bees optimization—a haplometrosis polygynous swarming approach

  29. Li X (2002) An optimizing method based on autonomous animats: Fish-swarm algorithm. Syst Eng Pract 22(11):32–38

    Google Scholar 

  30. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3):52–67

    MathSciNet  Google Scholar 

  31. Gordon N, Wagner IA, Bruckstein AM (2003) Discrete Bee dance algorithm for pattern formation on a grid. In: IEEE/WIC int. conf. intell. agent technol. IAT 2003, pp 545–549

  32. Lučić P, Teodorović D (2003) Computing with bees: attacking complex transportation engineering problems. Int J Artif Intell Tools 12(03):375–394

    Google Scholar 

  33. Jung SH (2003) Queen-bee evolution for genetic algorithms. Electron Lett 39(6):575–576

    Google Scholar 

  34. Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Resour Plan Manag 129(3):210–225

    Google Scholar 

  35. Wedde HF, Farooq M, Zhang Y (2004) BeeHive: an efficient fault-tolerant routing algorithm inspired by honey bee behavior. Springer, Berlin, pp 83–94

    Google Scholar 

  36. Teodorovic D, Dell’Orco M (2005) Bee colony optimization–a cooperative learning approach to complex transportation problems. In: Proceedings of the 16th mini-EURO conference on advanced OR and AI methods in transportation, Poznan, pp 51–60

  37. Drias H, Sadeg S, Yahi S (2005) Cooperative bees swarm for solving the maximum weighted satisfiability problem. Springer, Berlin, pp 318–325

    Google Scholar 

  38. Krishnanand KN, Ghose D (2005) Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings 2005 IEEE swarm intelligence symposium. SIS 2005, pp 84–91

  39. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, vol 200. Erciyes university, engineering faculty, computer engineering department, pp 1–10

  40. Yang X-S (2005) Engineering optimizations via nature-inspired virtual bee algorithms. Springer, Berlin, pp 317–323

    Google Scholar 

  41. Ghanbarzadeh A, Koç E, Otri S, Rahim S, Zaidi M (2006) The bees algorithm—a novel tool for complex optimisation problems. In: Intell. Prod. Mach. Syst, pp 454–459

  42. Chu S-C, Tsai P, Pan J-S (2006) Cat swarm optimization. Springer, Berlin, pp 854–858

    Google Scholar 

  43. Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1(4):355–366

    Google Scholar 

  44. Martin R, Stephen W (2006) Termite: a swarm intelligent routing algorithm for mobile wireless ad-hoc networks. Springer, Berlin, pp 155–184

    Google Scholar 

  45. Yang X-S, Lees JM, Morley CT (2006) Application of virtual ant algorithms in the optimization of CFRP shear strengthened precracked structures. Springer, Berlin, pp 834–837

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  47. Chen T-C, Tsai P-W, Chu S-C, Pan J-S (2007) a novel optimization approach: bacterial-GA foraging. In: Second international conference on innovative computing, information and control (ICICIC 2007)

  48. Su S, Wang J, Fan W, Yin X (2007) Good lattice swarm algorithm for constrained engineering design optimization. In: 2007 International conference on wireless communications, networking and mobile computing, pp 6415–6418

  49. Zhao RQ, Tang WS (2008) Monkey algorithm for global numerical optimization. J Uncertain Syst 2(3):165–176

    Google Scholar 

  50. Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 16:1–18

    Google Scholar 

  51. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Google Scholar 

  52. Chu Y, Mi H, Liao H, Ji Z, Wu QH (2008) A fast bacterial swarming algorithm for high-dimensional function optimization. In: 2008 IEEE congress on evolutionary computation (ieee world congress on computational intelligence), pp 3135–3140

  53. Bastos Filho CJA, de Lima Neto FB, Lins AJCC, Nascimento AIS, Lima MP (2008) A novel search algorithm based on fish school behavior. In: 2008 IEEE international conference on systems, man and cybernetics, pp 2646–2651

  54. Havens TC, Spain CJ, Salmon NG, Keller JM (2008) Roach infestation optimization. In: 2008 IEEE swarm intelligence symposium, pp 1–7

  55. Comellas F, Martinez-Navarro J (2009) Bumblebees. In: Proceedings of the first ACM/SIGEVO summit on genetic and evolutionary computation—GEC’09, p 811

  56. Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC), pp 210–214

  57. He S, Wu QH, Saunders JR (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13(5):973–990

    Google Scholar 

  58. Premaratne U, Samarabandu J, Sidhu T (2009) A new biologically inspired optimization algorithm. In: 2009 international conference on industrial and information systems (ICIIS), 2009, pp. 279–284

  59. Yang X-S (2010) A new metaheuristic bat-inspired algorithm. Springer, Berlin, pp 65–74

    MATH  Google Scholar 

  60. Iordache S (2010) Consultant-guided search. In: Proceedings of the 12th annual conference on genetic and evolutionary computation—GECCO’10, p 225

  61. Yang X-S, Deb S (2010) Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. Springer, Berlin, pp 101–111

    MATH  Google Scholar 

  62. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio Inspired Computation 2(2):78–84

    Google Scholar 

  63. Chen H, Zhu Y, Hu K, He X (2010) Hierarchical swarm model: a new approach to optimization. Discrete Dyn Nat Soc 2010:1–30

    MathSciNet  MATH  Google Scholar 

  64. Hedayatzadeh R, Akhavan Salmassi F, Keshtgari M, Akbari R, Ziarati K (2010) Termite colony optimization: a novel approach for optimizing continuous problems. In: 2010 18th Iranian conference on electrical engineering, pp 553–558

  65. Parpinelli RS, Lopes HS (2011) An eco-inspired evolutionary algorithm applied to numerical optimization. In: 2011 third world congress on nature and biologically inspired computing, pp 466–471

  66. Pan W-T (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74

    Google Scholar 

  67. Ting TO, Man KL, Guan S-U, Nayel M, Wan K (2012) Weightless swarm algorithm (WSA) for dynamic optimization problems. Springer, Berlin, pp 508–515

    Google Scholar 

  68. Civicioglu P (2013) Artificial cooperative search algorithm for numerical optimization problems. Inf Sci (Ny) 229:58–76

    MATH  Google Scholar 

  69. Yang X-S (2012) Flower pollination algorithm for global optimization. Springer, Berlin, pp 240–249

    MATH  Google Scholar 

  70. Hernández H, Blum C (2012) Distributed graph coloring: an approach based on the calling behavior of Japanese tree frogs. Swarm Intell 6(2):117–150

    Google Scholar 

  71. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    MathSciNet  MATH  Google Scholar 

  72. Mozaffari A, Fathi A, Behzadipour S (2012) The great salmon run: a novel bio-inspired algorithm for artificial system design and optimisation. Int J BioInspired Comput 4(5):286

    Google Scholar 

  73. Maia RD, de Castro LN, Caminhas WM (2012) Bee colonies as model for multimodal continuous optimization: the OptBees algorithm. IEEE Congr Evol Comput 2012:1–8

    Google Scholar 

  74. Tang R, Fong S, Yang XS, Deb S (2012) Wolf search algorithm with ephemeral memory. In: Seventh international conference on digital information management (ICDIM 2012). IEEE, pp 165–172

  75. Kaveh A, Farhoudi N (2013) A new optimization method: dolphin echolocation. Adv Eng Softw 59:53–70

    Google Scholar 

  76. Sur C, Sharma S, Shukla A (2013) Egyptian vulture optimization algorithm—a new nature inspired meta-heuristics for knapsack problem. Springer, Berlin, pp 227–237

    Google Scholar 

  77. Neshat M, Sepidnam G, Sargolzaei M (2013) Swallow swarm optimization algorithm: a new method to optimization. Neural Comput Appl 23(2):429–454

    Google Scholar 

  78. Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877

    Google Scholar 

  79. Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. Springer, Cham, pp 86–94

    Google Scholar 

  80. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  81. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Google Scholar 

  82. Uymaz SA, Tezel G, Yel E (2015) Artificial algae algorithm (AAA) for nonlinear global optimization. Appl Soft Comput 31:153–171

    Google Scholar 

  83. Meng X-B, Gao XZ, Lu L, Liu Y, Zhang H (2016) A new bio-inspired optimisation algorithm: bird swarm algorithm. J Exp Theor Artif Intell 28(4):673–687

    Google Scholar 

  84. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    Google Scholar 

  85. Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algorithm for optimization: virus colony search. Adv Eng Softw 92:65–88

    Google Scholar 

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

    Google Scholar 

  87. Yong W, Tao W, Cheng-Zhi Z, Hua-Juan H (2016) A new stochastic optimization approach—dolphin swarm optimization algorithm. Int J Comput Intell Appl 15(02):1650011

    Google Scholar 

  88. Abedinia O, Amjady N, Ghasemi A (2016) A new metaheuristic algorithm based on shark smell optimization. Complexity 21(5):97–116

    MathSciNet  Google Scholar 

  89. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  90. Qi X, Zhu Y, Zhang H (2017) A new meta-heuristic butterfly-inspired algorithm. J Comput Sci 23:226–239

    MathSciNet  Google Scholar 

  91. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Google Scholar 

  92. Jahani E, Chizari M (2018) Tackling global optimization problems with a novel algorithm—Mouth Brooding Fish algorithm. Appl Soft Comput 62:987–1002

    Google Scholar 

  93. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Google Scholar 

  94. Fausto F, Cuevas E, Valdivia A, González A (2017) A global optimization algorithm inspired in the behavior of selfish herds. Biosystems 160:39–55

    Google Scholar 

  95. Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70

    Google Scholar 

  96. Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148–175

    Google Scholar 

  97. Creutz M, Moriarty KJM (1983) Implementation of the microcanonical Monte Carlo simulation algorithm for SU(N) lattice gauge theory calculations. Comput Phys Commun 30(3):255–257

    Google Scholar 

  98. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    MathSciNet  MATH  Google Scholar 

  99. Bishop JM (1989) Stochastic searching networks. In: 1989 First IEE international conference on artificial neural networks, (Conf. Publ. No. 313), pp 329–331

  100. Vicsek T, Czirók A, Ben-Jacob E, Cohen I, Shochet O (1995) Novel type of phase transition in a system of self-driven particles. Phys Rev Lett 75(6):1226–1229

    MathSciNet  Google Scholar 

  101. Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24(11):1097–1100

    MathSciNet  MATH  Google Scholar 

  102. Linhares A (1998) Preying on optima: a predatory search strategy for combinatorial problems. In: SMC’98 conference proceedings. 1998 IEEE international conference on systems, man, and cybernetics (Cat. No. 98CH36218), vol 3, pp 2974–2978

  103. Murase H (2000) Finite element inverse analysis using a photosynthetic algorithm. Comput Electron Agric 29(1–2):115–123

    Google Scholar 

  104. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Google Scholar 

  105. Webster B, Bernhard PJ (2003) A local search optimization algorithm based on natural principles of gravitation. CS-2003-10, Florida Institute of Technology

  106. Erol OK, Eksin I (2006) A new optimization method: big bang-big crunch. Adv Eng Softw 37(2):106–111

    Google Scholar 

  107. Formato RA (2007) Central force optimization: a new metaheuristic with applications in applied electromagnetics. In: Progress in electromagnetics research. PIER 77, pp 425–491

  108. Hosseini HS (2007) Problem solving by intelligent water drops. IEEE Congr Evol Comput 2007:3226–3231

    Google Scholar 

  109. Rabanal P, Rodríguez I, Rubio F (2007) Using river formation dynamics to design heuristic algorithms. In: Unconventional computation. Springer, Berlin, pp 163–177

  110. Monismith DR, Mayfield BE (2008) Slime mold as a model for numerical optimization. In: 2008 IEEE swarm intelligence symposium. IEEE, pp 1–8

  111. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (Ny) 179(13):2232–2248

    MATH  Google Scholar 

  112. Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3–4):267–289

    MATH  Google Scholar 

  113. Cuevas E, Oliva D, Zaldivar D, Pérez-Cisneros M, Sossa H (2012) Circle detection using electro-magnetism optimization. Inf Sci (Ny) 182(1):40–55

    MathSciNet  Google Scholar 

  114. Shah-Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation Some of the authors of this publication are also working on these related projects: applications of population-based optimization methods View project Self-ception View project Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Artic Int J Comput Sci Eng 6(2):132–140

    Google Scholar 

  115. Tamura K, Yasuda K (2011) Spiral dynamics inspired optimization. J Adv Comput Intell Intell Inf 15(8):1116–1122

    Google Scholar 

  116. Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci (Ny) 222:175–184

    MathSciNet  Google Scholar 

  117. Moghaddam FF, Moghaddam RF, Cheriet M (2012) Curved space optimization: a random search based on general relativity theory. Neural Evol Comput

  118. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294

    Google Scholar 

  119. 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 110–111:151–166

    Google Scholar 

  120. Gao-Wei Y, Zhanju H (2012) A novel atmosphere clouds model optimization algorithm. In: 2012 international conference on computing, measurement, control and sensor network, pp 217–220

  121. 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(5):2592–2612

    Google Scholar 

  122. Kaveh A, Mahdavi VR (2014) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27

    Google Scholar 

  123. Moein S, Logeswaran R (2014) KGMO: a swarm optimization algorithm based on the kinetic energy of gas molecules. Inf Sci (Ny) 275:127–144

    MathSciNet  Google Scholar 

  124. Shareef H, Ibrahim AA, Mutlag AH (2015) Lightning search algorithm. Appl Soft Comput 36:315–333

    Google Scholar 

  125. Baykasoğlu A, Akpinar Ş (2017) Weighted superposition attraction (WSA): a swarm intelligence algorithm for optimization problems—part 1: unconstrained optimization. Appl Soft Comput 56:520–540

    Google Scholar 

  126. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133

    Google Scholar 

  127. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Google Scholar 

  128. Tabari A, Ahmad A (2017) A new optimization method: electro-search algorithm. Comput Chem Eng 103:1–11

    Google Scholar 

  129. Nematollahi AF, Rahiminejad A, Vahidi B (2017) A novel physical based meta-heuristic optimization method known as lightning attachment procedure optimization. Appl Soft Comput 59:596–621

    Google Scholar 

  130. Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84

    Google Scholar 

  131. Husseinzadeh Kashan A, Tavakkoli-Moghaddam R, Gen M (2019) Find-fix-finish-exploit-analyze (F3EA) meta-heuristic algorithm: an effective algorithm with new evolutionary operators for global optimization. Comput Ind Eng 128:192–218

    Google Scholar 

  132. Ray T, Liew KM (2003) Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput 7(4):386–396

    Google Scholar 

  133. Zhang LM, Dahlmann C, Zhang Y (2009) Human-inspired algorithms for continuous function optimization. In: 2009 IEEE international conference on intelligent computing and intelligent systems, pp 318–321

  134. Kashan AH (2009) League championship algorithm: a new algorithm for numerical function optimization. In: 2009 international conference of soft computing and pattern recognition, pp 43–48

  135. Xu Y, Cui Z, Zeng J (2010) Social emotional optimization algorithm for nonlinear constrained optimization problems. Springer, Berlin, pp 583–590

    Google Scholar 

  136. Shi Y (2011) Brain storm optimization algorithm. Springer, Berlin, pp 303–309

    Google Scholar 

  137. Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci (Ny) 183(1):1–15

    MathSciNet  Google Scholar 

  138. Shayeghi H, Dadashpour J (2012) Anarchic society optimization based PID control of an automatic voltage regulator (AVR) system. Electr Electron Eng 2(4):199–207

    Google Scholar 

  139. Moghdani R, Salimifard K (2018) Volleyball premier league algorithm. Appl Soft Comput 64:161–185

    Google Scholar 

  140. Awad NH, Ali MZ, Liang JJ, Qu BY, Suganthan PN (2016) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-parameter numerical optimization. In: Technical Report, Nanyang Technological University Singapore

  141. Das S, Suganthan PN (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Tech. Rep

  142. García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15(6):617–644

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, 12613, Egypt

    Ali Wagdy Mohamed

  2. Wireless Intelligent Networks Center (WINC), School of Engineering and Applied Sciences, Nile University, Giza, Egypt

    Ali Wagdy Mohamed

  3. College of Computing and Information Technology, King Abdulaziz University, P. O. Box 80200, Jeddah, 21589, Saudi Arabia

    Anas A. Hadi

  4. Department of Computer Science, Faculty of Computer Science, October University for Modern Sciences and Arts (MSA), 6th October City, Giza, 12451, Egypt

    Ali Khater Mohamed

Authors
  1. Ali Wagdy Mohamed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anas A. Hadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Khater Mohamed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Wagdy Mohamed.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 599 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohamed, A.W., Hadi, A.A. & Mohamed, A.K. Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. Int. J. Mach. Learn. & Cyber. 11, 1501–1529 (2020). https://doi.org/10.1007/s13042-019-01053-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-019-01053-x

Keywords

Search

Navigation