Skip to main content
SpringerLink
Log in

Honey formation optimization with single component for numerical function optimization: HFO-1

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Honey formation optimization (HFO) is originally proposed for design problems where the definitions of the objective functions are known priori or under design. HFO extends the Artificial Bee Colony (ABC) algorithm with the concept of multiple components in a source and the worker bees tending to collect components currently needed. However, the necessity of component design for a particular problem makes the HFO not applicable to optimize an arbitrary objective function. In this paper, HFO with single component (HFO-1) is proposed in order to remove this hardship of HFO for numerical function optimizations. Unlike the HFO, which only models the honey formation inside the bee, the HFO-1 further model the honey production process in the hive where sources are turned into honey-forms inside the bee and mature in time through various types of mixing processes using enzymes until the whole mixture becomes mature in the hive. During mixing process, the \(Pbest\) (population best) is used as primal catalyzer that metamorphoses other forms towards itself. When the current mixture is mature, a new mixture is started from a new site and saturated with the \(Gbest\) (global best) to fasten the maturity of the new mixture towards \(Gbest\). HFO-1 is original in that it extends the formation phase of HFO with novel local search and importantly introduces 3 new phases, mixing, maturation, and saturation, specific to honey production. In this article, 6 algorithms (Whale Optimization, Differential Search, Particle Swarm Optimization, Improved Grey Wolf, Moth-Flame Optimization, HFO-1) are comparatively studied on the basis of 60 popular benchmark functions, containing CEC2019 functions. The results show that HFO-1 is superior to others according to mean absolute error, mean variance and Wilcoxon Rank-Sum Test analysis.

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

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

References

  1. Ma Q, Meng Q, Xu S (2023) Distributed optimization for uncertain high-order nonlinear multiagent systems via dynamic gain approach. IEEE Trans Syst Man Cybern Syst 53:4351–4357. https://doi.org/10.1109/TSMC.2023.3247456

    Article  Google Scholar 

  2. Duan Y, Zhao Y, Hu J (2023) An initialization-free distributed algorithm for dynamic economic dispatch problems in microgrid: modeling, optimization and analysis. Sustain Energy Grids Netw 34:101004. https://doi.org/10.1016/j.segan.2023.101004

    Article  Google Scholar 

  3. Zhang J, Tang Y, Wang H, Xu K (2023) ASRO-DIO: active subspace random optimization based depth inertial odometry. IEEE Trans Robot 39:1496–1508. https://doi.org/10.1109/TRO.2022.3208503

    Article  Google Scholar 

  4. Kumar A, Misra RK, Singh D (2017) Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase. In: 2017 IEEE congress on evolutionary computation CEC 2017—proceedings, pp 1835–1842. https://doi.org/10.1109/CEC.2017.7969524

  5. Huang H, Xue C, Zhang W, Guo M (2022) Torsion design of CFRP-CFST columns using a data-driven optimization approach. Eng Struct 251:113479. https://doi.org/10.1016/j.engstruct.2021.113479

    Article  Google Scholar 

  6. Zhang K, Wang Z, Chen G et al (2022) Training effective deep reinforcement learning agents for real-time life-cycle production optimization. J Pet Sci Eng 208:109766. https://doi.org/10.1016/j.petrol.2021.109766

    Article  Google Scholar 

  7. Mohamed AW, Sallam KM, Agrawal P et al (2023) Evaluating the performance of meta-heuristic algorithms on CEC 2021 benchmark problems. Neural Comput Appl 35:1493–1517. https://doi.org/10.1007/s00521-022-07788-z

    Article  Google Scholar 

  8. Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann Arbor

    MATH  Google Scholar 

  9. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359. https://doi.org/10.1023/A:1008202821328/METRICS

    Article  MathSciNet  MATH  Google Scholar 

  10. Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of ICNN'95—international conference on neural networks, vol 4, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  11. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26:29–41. https://doi.org/10.1109/3477.484436

    Article  Google Scholar 

  12. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Kayseri

  13. Cao B, Zhao J, Gu Y et al (2020) Security-aware industrial wireless sensor network deployment optimization. IEEE Trans Ind Inform 16:5309–5316. https://doi.org/10.1109/TII.2019.2961340

    Article  Google Scholar 

  14. Cao B, Zhao J, Yang P et al (2020) Multiobjective 3-D topology optimization of next-generation wireless data center network. IEEE Trans Ind Inform 16:3597–3605. https://doi.org/10.1109/TII.2019.2952565

    Article  Google Scholar 

  15. Aydogdu I, Ormecioglu TO, Tunca O, Carbas S (2022) Design of large-scale real-size steel structures using various modified grasshopper optimization algorithms. Neural Comput Appl 34:13825–13848. https://doi.org/10.1007/S00521-022-07196-3/FIGURES/13

    Article  Google Scholar 

  16. Rajmohan S, Elakkiya E, Sreeja SR (2022) Multi-cohort whale optimization with search space tightening for engineering optimization problems. Neural Comput Appl. https://doi.org/10.1007/S00521-022-08139-8/FIGURES/9

    Article  Google Scholar 

  17. Abualigah L, Ewees AA, Al-qaness MAA et al (2022) Boosting arithmetic optimization algorithm by sine cosine algorithm and levy flight distribution for solving engineering optimization problems. Neural Comput Appl 34:8823–8852. https://doi.org/10.1007/S00521-022-06906-1/TABLES/8

    Article  Google Scholar 

  18. Na S, Niu H, Lennox B, Arvin F (2022) Bio-inspired collision avoidance in swarm systems via deep reinforcement learning. IEEE Trans Veh Technol 71:2511–2526. https://doi.org/10.1109/TVT.2022.3145346

    Article  Google Scholar 

  19. Wang H, Dong C, Fu Y (2021) Optimization analysis of sport pattern driven by machine learning and multi-agent. Neural Comput Appl 33:1067–1077. https://doi.org/10.1007/S00521-020-05022-2/FIGURES/11

    Article  Google Scholar 

  20. Keserwani PK, Govil MC, Pilli ES (2021) An effective NIDS framework based on a comprehensive survey of feature optimization and classification techniques. Neural Comput Appl 35:4993–5013. https://doi.org/10.1007/S00521-021-06093-5/TABLES/10

    Article  Google Scholar 

  21. Li X, Sun Y (2020) Stock intelligent investment strategy based on support vector machine parameter optimization algorithm. Neural Comput Appl 32:1765–1775. https://doi.org/10.1007/s00521-019-04566-2

    Article  Google Scholar 

  22. Wiktorowicz K, Krzeszowski T, Przednowek K (2021) Sparse regressions and particle swarm optimization in training high-order Takagi-Sugeno fuzzy systems. Neural Comput Appl 33:2705–2717. https://doi.org/10.1007/S00521-020-05133-W/FIGURES/5

    Article  Google Scholar 

  23. Qaraad M, Amjad S, Hussein NK, Elhosseini MA (2022) Large scale salp-based grey wolf optimization for feature selection and global optimization. Neural Comput Appl 34:8989–9014. https://doi.org/10.1007/S00521-022-06921-2/TABLES/21

    Article  Google Scholar 

  24. Akinola OO, Ezugwu AE, Agushaka JO et al (2022) Multiclass feature selection with metaheuristic optimization algorithms: a review. Neural Comput Appl 3422(34):19751–19790. https://doi.org/10.1007/S00521-022-07705-4

    Article  Google Scholar 

  25. Li Y, Hei X (2022) Performance optimization of computing task scheduling based on the Hadoop big data platform. Neural Comput Appl. https://doi.org/10.1007/S00521-022-08114-3/FIGURES/9

    Article  Google Scholar 

  26. Selvanambi R, Natarajan J, Karuppiah M et al (2022) Retraction note: lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization (Neural Computing and Applications, (2020), 32, (4373–4386), DOI: 10.1007/s00521-018-3824-3). Neural Comput Appl 35:3571–3571. https://doi.org/10.1007/S00521-022-08142-Z/METRICS

    Article  Google Scholar 

  27. Sowan B, Eshtay M, Dahal K et al (2022) Hybrid PSO feature selection-based association classification approach for breast cancer detection. Neural Comput Appl 35:5291–5317. https://doi.org/10.1007/S00521-022-07950-7/TABLES/7

    Article  Google Scholar 

  28. Nasr AA (2022) A new cloud autonomous system as a service for multi-mobile robots. Neural Comput Appl 34:21223–21235. https://doi.org/10.1007/S00521-022-07605-7/FIGURES/13

    Article  Google Scholar 

  29. Wang B, Liao X (2023) A trusted routing mechanism for multi-attribute chain energy optimization for Industrial Internet of Things. Neural Comput Appl 2023:1–11. https://doi.org/10.1007/S00521-023-08215-7

    Article  Google Scholar 

  30. Nematzadeh S, Torkamanian-Afshar M, Seyyedabbasi A, Kiani F (2022) Maximizing coverage and maintaining connectivity in WSN and decentralized IoT: an efficient metaheuristic-based method for environment-aware node deployment. Neural Comput Appl 351(35):611–641. https://doi.org/10.1007/S00521-022-07786-1

    Article  Google Scholar 

  31. Sasirekha K, Thangavel K (2019) Optimization of K-nearest neighbor using particle swarm optimization for face recognition. Neural Comput Appl 31:7935–7944. https://doi.org/10.1007/S00521-018-3624-9/FIGURES/9

    Article  Google Scholar 

  32. Karkinli AE (2022) Detection of object boundary from point cloud by using multi-population based differential evolution algorithm. Neural Comput Appl 35:5193–5206. https://doi.org/10.1007/S00521-022-07969-W/FIGURES/4

    Article  Google Scholar 

  33. Yiğit H, Ürgün S, Mirjalili S (2022) Comparison of recent metaheuristic optimization algorithms to solve the SHE optimization problem in MLI. Neural Comput Appl. https://doi.org/10.1007/S00521-022-07980-1/FIGURES/13

    Article  Google Scholar 

  34. Nordin NS, Ismail MA (2022) A hybridization of butterfly optimization algorithm and harmony search for fuzzy modelling in phishing attack detection. Neural Comput Appl 35:5501–5512. https://doi.org/10.1007/S00521-022-07957-0/TABLES/6

    Article  Google Scholar 

  35. Oun A, Hazari NA, Niamat MY (2021) Analysis of swarm intelligence based ANN algorithms for attacking PUFs. IEEE Access 9:121743–121758. https://doi.org/10.1109/ACCESS.2021.3109235

    Article  Google Scholar 

  36. Yang X-S (2012) Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) UCNC 2012. Lecture notes in computer science. Springer, Berlin, pp 29–40

    Google Scholar 

  37. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249. https://doi.org/10.1016/J.KNOSYS.2015.07.006

    Article  Google Scholar 

  38. Mirjalili S, Lewis A (2016) The Whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/J.ADVENGSOFT.2016.01.008

    Article  Google Scholar 

  39. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/J.ADVENGSOFT.2013.12.007

    Article  Google Scholar 

  40. Jin J, Wang P (2021) Multiscale quantum harmonic oscillator algorithm with guiding information for single objective optimization. Swarm Evol Comput 65:100916. https://doi.org/10.1016/J.SWEVO.2021.100916

    Article  Google Scholar 

  41. Sallam KM, Abdel-Basset M, El-Abd M, Wagdy A (2022) IMODEII: an improved IMODE algorithm based on the reinforcement learning. In: 2022 IEEE congress on evolutionary computation CEC 2022—conference proceedings, pp 1–8. https://doi.org/10.1109/CEC55065.2022.9870420

  42. Kiran MS, Gündüz M (2013) A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems. Appl Soft Comput 13:2188–2203. https://doi.org/10.1016/J.ASOC.2012.12.007

    Article  Google Scholar 

  43. Chen X, Tianfield H, Du W (2021) Bee-foraging learning particle swarm optimization. Appl Soft Comput 102:107134. https://doi.org/10.1016/J.ASOC.2021.107134

    Article  Google Scholar 

  44. Stephan P, Stephan T, Kannan R, Abraham A (2021) A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis. Neural Comput Appl 33:13667–13691. https://doi.org/10.1007/S00521-021-05997-6/TABLES/16

    Article  Google Scholar 

  45. Gao H, Li H, Liu Y et al (2020) High-quality-guided artificial bee colony algorithm for designing loudspeaker. Neural Comput Appl 32:4473–4480. https://doi.org/10.1007/S00521-018-3568-0/FIGURES/7

    Article  Google Scholar 

  46. Sharma S, Sharma H, Sharma JB, Poonia RC (2021) A secure and robust color image watermarking using nature-inspired intelligence. Neural Comput Appl 35:4919–4937. https://doi.org/10.1007/S00521-020-05634-8/TABLES/8

    Article  Google Scholar 

  47. Alrosan A, Alomoush W, Norwawi N et al (2021) An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation. Neural Comput Appl 33:1671–1697. https://doi.org/10.1007/S00521-020-05118-9/FIGURES/17

    Article  Google Scholar 

  48. Houssein EH, Hosney ME, Mohamed WM et al (2022) Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data. Neural Comput Appl 35:5251–5275. https://doi.org/10.1007/S00521-022-07916-9/TABLES/7

    Article  Google Scholar 

  49. Korkmaz E (2023) Energy demand estimation in Turkey according to modes of transportation: Bezier search differential evolution and black widow optimization algorithms-based model development and application. Neural Comput Appl. https://doi.org/10.1007/S00521-023-08245-1/FIGURES/8

    Article  Google Scholar 

  50. Arık OA (2021) Artificial bee colony algorithm including some components of iterated greedy algorithm for permutation flow shop scheduling problems. Neural Comput Appl 33:3469–3486. https://doi.org/10.1007/S00521-020-05174-1/TABLES/7

    Article  Google Scholar 

  51. Gupta S, Deep K (2020) Hybrid sine cosine artificial bee colony algorithm for global optimization and image segmentation. Neural Comput Appl 32:9521–9543. https://doi.org/10.1007/S00521-019-04465-6/TABLES/10

    Article  Google Scholar 

  52. Kumar D, Mishra KK (2017) Portfolio optimization using novel co-variance guided Artificial Bee Colony algorithm. Swarm Evol Comput 33:119–130. https://doi.org/10.1016/J.SWEVO.2016.11.003

    Article  Google Scholar 

  53. Kumar R, Kumar P, Kumar Y (2022) Three stage fusion for effective time series forecasting using Bi-LSTM-ARIMA and improved DE-ABC algorithm. Neural Comput Appl 34:18421–18437. https://doi.org/10.1007/S00521-022-07431-X/TABLES/8

    Article  Google Scholar 

  54. Latifoğlu F (2020) A novel singular spectrum analysis-based multi-objective approach for optimal FIR filter design using artificial bee colony algorithm. Neural Comput Appl 32:13323–13341. https://doi.org/10.1007/S00521-019-04680-1/TABLES/8

    Article  Google Scholar 

  55. Kumar Y, Sahoo G (2017) A two-step artificial bee colony algorithm for clustering. Neural Comput Appl 28:537–551. https://doi.org/10.1007/S00521-015-2095-5/TABLES/11

    Article  Google Scholar 

  56. Hancer E (2022) A multi-objective artificial bee colony algorithm for cost-sensitive subset selection. Neural Comput Appl 34:17523–17537. https://doi.org/10.1007/S00521-022-07407-X/TABLES/3

    Article  Google Scholar 

  57. Asteris PG, Nikoo M (2019) Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Comput Appl 31:4837–4847. https://doi.org/10.1007/S00521-018-03965-1/FIGURES/7

    Article  Google Scholar 

  58. Karaboga D, Gorkemli B (2014) A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Appl Soft Comput 23:227–238. https://doi.org/10.1016/J.ASOC.2014.06.035

    Article  Google Scholar 

  59. Aslan S, Badem H, Karaboga D (2019) Improved quick artificial bee colony (iqABC) algorithm for global optimization. Soft Comput 23:13161–13182. https://doi.org/10.1007/S00500-019-03858-Y/FIGURES/3

    Article  Google Scholar 

  60. Mohamed AAA, Mohamed YS, El-Gaafary AAM, Hemeida AM (2017) Optimal power flow using moth swarm algorithm. Electr Power Syst Res 142:190–206. https://doi.org/10.1016/J.EPSR.2016.09.025

    Article  Google Scholar 

  61. Mirjalili S, Gandomi AH, Mirjalili SZ et al (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/J.ADVENGSOFT.2017.07.002

    Article  Google Scholar 

  62. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 22:52–67. https://doi.org/10.1109/MCS.2002.1004010

    Article  Google Scholar 

  63. Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70. https://doi.org/10.1016/J.ADVENGSOFT.2017.05.014

    Article  Google Scholar 

  64. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23:715–734. https://doi.org/10.1007/S00500-018-3102-4/FIGURES/10

    Article  Google Scholar 

  65. Chu S-C, Tsai P, Pan J-S (2006) Cat swarm optimization. In: Yang Q, Webb G (eds) PRICAI 2006: trends in artificial intelligence. PRICAI 2006. Lecture notes in computer science. Springer, Berlin, pp 854–858

  66. Salgotra R, Singh U (2019) The naked mole-rat algorithm. Neural Comput Appl 31:8837–8857. https://doi.org/10.1007/S00521-019-04464-7/TABLES/5

    Article  Google Scholar 

  67. Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148–175. https://doi.org/10.1016/J.SWEVO.2018.02.013

    Article  Google Scholar 

  68. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 World congress on nature & biologically inspired computing, NABIC 2009—proceedings, pp 210–214. https://doi.org/10.1109/NABIC.2009.5393690

  69. Heidari AA, Mirjalili S, Faris H et al (2019) Harris Hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872. https://doi.org/10.1016/J.FUTURE.2019.02.028

    Article  Google Scholar 

  70. Yang X-SS (2010) A new betaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C et al (eds) A new metaheuristic bat-inspired algorithm BT—nature inspired cooperative strategies for optimization (NICSO 2010) studies in computational intelligence. Springer, Berlin, pp 65–74

    Google Scholar 

  71. Abdullah JM, Ahmed T (2019) Fitness dependent optimizer: inspired by the bee swarming reproductive process. IEEE Access 7:43473–43486. https://doi.org/10.1109/ACCESS.2019.2907012

    Article  Google Scholar 

  72. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspir Comput 2:78–84. https://doi.org/10.48550/arxiv.1003.1409

    Article  Google Scholar 

  73. Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Tavakkoli-Moghaddam R (2020) Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft Comput 24:14637–14665. https://doi.org/10.1007/S00500-020-04812-Z/FIGURES/17

    Article  Google Scholar 

  74. Covic N, Lacevic B (2020) Wingsuit flying search-a novel global optimization algorithm. IEEE Access 8:53883–53900. https://doi.org/10.1109/ACCESS.2020.2981196

    Article  Google Scholar 

  75. Kaur S, Awasthi LK, Sangal AL, Dhiman G (2020) Tunicate swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell 90:103541. https://doi.org/10.1016/J.ENGAPPAI.2020.103541

    Article  Google Scholar 

  76. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17:4831–4845. https://doi.org/10.1016/J.CNSNS.2012.05.010

    Article  MathSciNet  MATH  Google Scholar 

  77. Elrahman ASA, Hefny HA (2020) Vortex swarm optimization: new metaheuristic algorithm. In: Hassanien AE, Azar A, Gaber T, Oliva DT (eds) International conference on artificial intelligence and computer vision (AICV2020). AICV 2020. Advances in intelligent systems and computing. Springer, pp 127–136

  78. Civicioglu P (2012) Transforming geocentric Cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci 46:229–247. https://doi.org/10.1016/J.CAGEO.2011.12.011

    Article  Google Scholar 

  79. Chatterjee S, Dawn S, Hore S (2020) Artificial cell swarm optimization. In: Khosravy M, Gupta N, Patel N, Senjyu T (eds) Frontier applications of nature inspired computation. Springer tracts in nature-inspired computing. Springer, Singapore, pp 196–214

    Chapter  Google Scholar 

  80. Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. In: Tan Y, Shi Y, Coello CAC (eds) Advances in swarm intelligence. ICSI 2014. Lecture notes in computer science. Springer, pp 86–94

  81. Drias H, Drias Y, Khennak I (2020) A new swarm algorithm based on orcas intelligence for solving maze problems. In: Rocha Á, Adeli H, Reis L, Costanzo S, Orovic I, Moreira F (eds) Trends and innovations in information systems and technologies. WorldCIST 2020. Advances in intelligent systems and computing. Springer, pp 788–797

  82. Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377. https://doi.org/10.1016/J.ESWA.2020.113377

    Article  Google Scholar 

  83. Bansal JC, Sharma H, Jadon SS, Clerc M (2014) Spider monkey optimization algorithm for numerical optimization. Memetic Comput 6:31–47. https://doi.org/10.1007/S12293-013-0128-0/TABLES/4

    Article  Google Scholar 

  84. Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021) African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng 158:107408. https://doi.org/10.1016/J.CIE.2021.107408

    Article  Google Scholar 

  85. Xie L, Han T, Zhou H et al (2021) Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization. Comput Intell Neurosci. https://doi.org/10.1155/2021/9210050

    Article  Google Scholar 

  86. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/J.ADVENGSOFT.2015.01.010

    Article  Google Scholar 

  87. Dhiman G, Garg M, Nagar A et al (2021) A novel algorithm for global optimization: rat swarm optimizer. J Ambient Intell Humaniz Comput 12:8457–8482. https://doi.org/10.1007/S12652-020-02580-0/TABLES/22

    Article  Google Scholar 

  88. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053–1073. https://doi.org/10.1007/S00521-015-1920-1/TABLES/12

    Article  Google Scholar 

  89. Yetgin Z, Abaci H (2021) Honey formation optimization framework for design problems. Appl Math Comput 394:125815. https://doi.org/10.1016/J.AMC.2020.125815

    Article  MathSciNet  MATH  Google Scholar 

  90. Meng XB, Gao XZ, Lu L et al (2015) A new bio-inspired optimisation algorithm: bird swarm algorithm. J Exp Theor Artif Intell 28:673–687. https://doi.org/10.1080/0952813X.2015.1042530

    Article  Google Scholar 

  91. Abdollahzadeh B, Soleimanian Gharehchopogh F, Mirjalili S (2021) Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Int J Intell Syst 36:5887–5958. https://doi.org/10.1002/INT.22535

    Article  Google Scholar 

  92. Zhao W, Wang L, Mirjalili S (2022) Artificial hummingbird algorithm: a new bio-inspired optimizer with its engineering applications. Comput Methods Appl Mech Eng 388:114194. https://doi.org/10.1016/J.CMA.2021.114194

    Article  MathSciNet  MATH  Google Scholar 

  93. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12. https://doi.org/10.1016/J.COMPSTRUC.2016.03.001

    Article  Google Scholar 

  94. Akbari MA, Zare M, Azizipanah-abarghooee R et al (2022) (2022) The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems. Sci Rep 121(12):1–20. https://doi.org/10.1038/s41598-022-14338-z

    Article  Google Scholar 

  95. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/J.ADVENGSOFT.2017.01.004

    Article  Google Scholar 

  96. Alimoradi M, Azgomi H, Asghari A (2022) Trees social relations optimization algorithm: a new swarm-based metaheuristic technique to solve continuous and discrete optimization problems. Math Comput Simul 194:629–664. https://doi.org/10.1016/J.MATCOM.2021.12.010

    Article  MathSciNet  MATH  Google Scholar 

  97. Chang T, Kong D, Hao N et al (2018) Solving the dynamic weapon target assignment problem by an improved artificial bee colony algorithm with heuristic factor initialization. Appl Soft Comput 70:845–863. https://doi.org/10.1016/J.ASOC.2018.06.014

    Article  Google Scholar 

  98. Xiang W, Li Y, Meng X et al (2017) A grey artificial bee colony algorithm. Appl Soft Comput 60:1–17. https://doi.org/10.1016/J.ASOC.2017.06.015

    Article  Google Scholar 

  99. Kaya E, Kaya CB (2021) A novel neural network training algorithm for the iddentification of nonlinear static systems: artificial bee colony algorithm based on effective scout bee stage. Symmetry 13:419. https://doi.org/10.3390/SYM13030419

    Article  Google Scholar 

  100. Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217:3166–3173. https://doi.org/10.1016/J.AMC.2010.08.049

    Article  MathSciNet  MATH  Google Scholar 

  101. Xu F, Li H, Pun CM et al (2020) A new global best guided artificial bee colony algorithm with application in robot path planning. Appl Soft Comput 88:106037. https://doi.org/10.1016/J.ASOC.2019.106037

    Article  Google Scholar 

  102. Kumar D, Mishra KK (2018) Co-variance guided artificial bee colony. Appl Soft Comput 70:86–107. https://doi.org/10.1016/J.ASOC.2018.04.050

    Article  Google Scholar 

  103. Lu R, Hu H, Xi M et al (2019) An improved artificial bee colony algorithm with fast strategy, and its application. Comput Electr Eng 78:79–88. https://doi.org/10.1016/J.COMPELECENG.2019.06.021

    Article  Google Scholar 

  104. Awadallah MA, Al-Betar MA, Bolaji AL et al (2019) Natural selection methods for artificial bee colony with new versions of onlooker bee. Soft Comput 23:6455–6494. https://doi.org/10.1007/S00500-018-3299-2/TABLES/31

    Article  Google Scholar 

  105. Li G, Cui L, Fu X et al (2017) Artificial bee colony algorithm with gene recombination for numerical function optimization. Appl Soft Comput 52:146–159. https://doi.org/10.1016/J.ASOC.2016.12.017

    Article  Google Scholar 

  106. Kong D, Chang T, Dai W et al (2018) An improved artificial bee colony algorithm based on elite group guidance and combined breadth-depth search strategy. Inf Sci (NY) 442–443:54–71. https://doi.org/10.1016/J.INS.2018.02.025

    Article  MathSciNet  Google Scholar 

  107. Gao W, Liu S (2011) Improved artificial bee colony algorithm for global optimization. Inf Process Lett 111:871–882. https://doi.org/10.1016/J.IPL.2011.06.002

    Article  MathSciNet  MATH  Google Scholar 

  108. Gao WF, Huang LL, Liu SY, Dai C (2015) Artificial bee colony algorithm based on information learning. IEEE Trans Cybern 45:2827–2839. https://doi.org/10.1109/TCYB.2014.2387067

    Article  Google Scholar 

  109. Xiao S, Wang H, Wang W et al (2021) Artificial bee colony algorithm based on adaptive neighborhood search and Gaussian perturbation. Appl Soft Comput 100:106955. https://doi.org/10.1016/J.ASOC.2020.106955

    Article  Google Scholar 

  110. Yu G, Zhou H, Wang H (2019) Improving artificial bee colony algorithm using a dynamic reduction strategy for dimension perturbation. Math Probl Eng. https://doi.org/10.1155/2019/3419410

    Article  Google Scholar 

  111. Zeng T, Ye T, Zhang L et al (2021) Population diversity guided dimension perturbation for artificial bee colony algorithm. Commun Comput Inf Sci 1449:473–485. https://doi.org/10.1007/978-981-16-5188-5_34/COVER

    Article  Google Scholar 

  112. Wang H, Haasis H-D, Su M et al (2022) Improved artificial bee colony algorithm for air freight station scheduling. MBE 19:13007–13027. https://doi.org/10.3934/mbe.2022607

    Article  Google Scholar 

  113. Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci (NY) 192:120–142. https://doi.org/10.1016/J.INS.2010.07.015

    Article  Google Scholar 

  114. Wang H, Wang W, Xiao S, et al (2019) Multi-strategy and dimension perturbation ensemble of artificial bee colony. In: 2019 IEEE congress on evolutionary computation CEC 2019—proceedings, pp 697–704. https://doi.org/10.1109/CEC.2019.8790129

  115. Zhou J, Yao X (2017) Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing. Appl Soft Comput 56:379–397. https://doi.org/10.1016/J.ASOC.2017.03.017

    Article  Google Scholar 

  116. Zhou J, Yao X (2017) Multi-objective hybrid artificial bee colony algorithm enhanced with Lévy flight and self-adaption for cloud manufacturing service composition. Appl Intell 47:721–742. https://doi.org/10.1007/S10489-017-0927-Y/FIGURES/13

    Article  Google Scholar 

  117. Yetgin Z, Şamdan M (2021) Honey formation optimization: HFO. Turk J Eng 5:81–88. https://doi.org/10.31127/TUJE.693103

    Article  Google Scholar 

  118. Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:1–25. https://doi.org/10.1016/j.eswa.2020.113917

    Article  Google Scholar 

  119. Li Y, Zhu X, Liu J (2020) An improved moth-flame optimization algorithm for engineering problems. Symmetry 12:1–30. https://doi.org/10.3390/SYM12081234

    Article  Google Scholar 

Download references

Funding

No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Mersin University, Mersin, Turkey

    Zeki Yetgin

  2. Department of Informatics, Akdeniz University, Antalya, Turkey

    Uğur Ercan

Authors
  1. Zeki Yetgin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Uğur Ercan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Uğur Ercan.

Ethics declarations

Conflict of interest

All authors declare that they have no conflicts of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yetgin, Z., Ercan, U. Honey formation optimization with single component for numerical function optimization: HFO-1. Neural Comput & Applic 35, 24897–24923 (2023). https://doi.org/10.1007/s00521-023-08984-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08984-1

Keywords

Search

Navigation