Abstract
Since artificial bee colony (ABC) algorithm, one of swarm intelligent algorithms, was proposed, it has shown good superiority in addressing optimization problems, and has attracted widespread attention because of its simple structure and good global optimization ability. However, ABC still has the shortcomings of slower convergence and poorer exploitation for complex practical problems. To overcome these limitations, an enhanced algorithm of multi-strategy collaboration based on neighborhood search called EMABC-NS is proposed. Firstly, the information of global optimal individual in the current population and individuals in the neighborhood are employed to the search phase of employed bees and onlooker bees, respectively. Secondly, the modification rate MR is introduced to randomly perturb all dimensions of the solutions. Finally, the search strategy of scout bees is enhanced by integrating current optimal solution and stochastic solution through MR. 23 well-established benchmark functions and 5 engineering optimization problems are utilized to validate the performance of EMABC-NS. The experimental result reveals that EMABC-NS is more competitiveness compared with other outstanding competitors, and it ranks first in the Friedman test. Compared with the other five algorithms, the proposed algorithm is also proved to be effective in solving practical engineering problems.
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Aguirre AM, Liu S, Papageorgiou LG (2018) Optimization approaches for supply chain planning and scheduling under demand uncertainty. Chem Eng Res Des 138:341–357
Akay B, Karaboga D (2012) A modified Artificial Bee Colony algorithm for real-parameter optimization. Inf Sci 192:120–142
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Comput Struct 169:1–12
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
Aydin D, Özyön S, Yaşar C et al (2014) Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem. Int J Electr Power Energy Syst 54:144–153
Banharnsakun A (2019) Artificial bee colony algorithm for enhancing image edge detection. Evol Syst 10:679–387
Banharnsakun A, Achalakul T, Sirinaovakul B (2011) The best-so-far selection in Artificial Bee Colony algorithm. Appl Soft Comput 11(2):2888–2901
Barshandeh S, Piri F, Sangani SR (2022) HMPA: an innovative hybrid multi-population algorithm based on artificial ecosystem-based and Harris Hawks optimization algorithms for engineering problems. Eng Comput 38(2):1581–1625
Baykasoğlu A, Özbakır L, Tapkan P (2007) Artificial bee colony algorithm and its application to generalized assignment problem. InTech, London
Cao J, Yin B, Lu X et al (2018) A modified artificial bee colony approach for the 0–1 knapsack problem. Appl Intell 48:1582–1595
Cao Y, Ji S, Lu Y (2020) An improved artificial bee colony algorithm with opposition-based learning. IET Image Proc 14(15):3639–3650
Chen L, Li Z, Zhang Y et al (2020) An improved quantum particle swarm photovoltaic multi-peak mPPT method combined with Lévy flight. Energy Sci Eng 8(11):3980–3994
Coello C (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287
Cui L, Li G, Wang X et al (2017) A ranking-based adaptive Artificial Bee Colony algorithm for global numerical optimization. Inf Sci 417:169–185
Cui L, Li G, Luo Y et al (2018) An enhanced artificial bee colony algorithm with dual-population framework. Swarm Evol Comput 43:184–206
Ding H, Feng Q (2009) Artificial bee colony algorithm based on Boltzmann selection policy. Comput Eng Appl 45(31):53–55
Dorigo M (1992) Optimization, learning and natural algorithms. Thesis Politecnico Di Milano Italy
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.
Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the frog leaping algorithm. J Water Resour Plan Manag 129(3):210–225
Gao W, Liu S (2011) Improved artificial bee colony algorithm for global optimization. Inf Process Lett 111(17):871–882
Gao W, Liu S (2012) A modified artificial bee colony algorithm. Comput Oper Res 39(3):687–697
Gao W, Liu S, Huang L (2012) A global best artifificial bee colony algorithm for global optimization. Comput Appl Math 236(11):2741–2753
Gao W, Liu S, Huang L (2013) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans Cybern 43(3):1011–1024
Goudarzi S, Wan HH, Anisi MH et al (2017) ABC-PSO for vertical handover in heterogeneous wireless networks. Neurocomputing 256:63–81
Han X, Yue L, Dong Y et al (2020) Efficient hybrid algorithm based on moth search and fireworks algorithm for solving numerical and constrained engineering optimization problems. J Supercomput 76:9404–9429
Hu P, Deng C, Wu Z (2019) Best neighbor-guided artificial bee colony algorithm for continuous optimization problems. Soft Comput 23:8723–8740
Karaboga D (2005) An idea based on honey Bee swarm for numerical optimization, Technical Report—TR06
Karaboga D, Gorkemli B (2014) A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Appl Soft Comput 23:227–238
Kiran SM, Hakli H, Gunduz M et al (2015) Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf Sci 300:140–157
Kwan HK, Raju R (2021) Design of p-norm linear phase FIR differentiators using adaptive modification rate artificial bee colony algorithm. IET Signal Proc 14(10):803–811
Ozturk C, Hancer E, Karaboga D (2015) A novel binary artificial bee colony algorithm based on genetic operators. Inf Sci 297:154–170
Pan QK (2016) An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling. Eur J Oper Res 250(3):702–714
Peng B, Wu L, Wang Y et al (2021) Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Inf Sci 578:214–235
Pu SA, Hao LB, Yong ZA et al (2021) An intensify atom search optimization for engineering design problems. Appl Math Model 89:837–859
Rao RS, Narasimham SVL, Ramalingaraju M (2011) Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm. Proc World Acad ENCE Eng Technol 45:116–122
Shao P, Yang L, Tan L et al (2020) Enhancing artificial bee colony algorithm using refraction principle. Soft Comput 24:15291–15306
Shukla S, Jain M (2019) A novel system for effective speech recognition based on artificial neural network and opposition artificial bee colony algorithm. Int J Speech Technol 22:959–969
Tang J, Xiong X (2019) A new artificial bee colony based on neighbourhood selection. Int J Innovative Comput Appl 10(1):12–17
Tsai HC (2019) Artificial bee colony directive for continuous optimization. Appl Soft Comput 87:1568–4946
Wang H, Wu Z, Rahnamayan S et al (2014) Multi-strategy ensemble artificial bee colony algorithm. Inf Sci 279:587–603
Wang H, Hu Z, Sun Y et al (2019) A novel modified BSA inspired by species evolution rule and simulated annealing principle for constrained engineering optimization problems. Neural Comput Appl 31:4157–4184
Xiao S, Wang W, Wang H et al (2019) An improved artificial bee colony algorithm based on elite strategy and dimension learning. Mathematics 7(3):289
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(3):106955
Xue Y, Jiang J, Zhao B et al (2018) A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput 22:2938–2952
Xue Y, Xue B, Zhang M (2019) Self-adaptive particle swarm optimization for large-scale feature selection in classification. ACM Trans Knowl Discov Data 13(5):1–27
Xue Y, Wang Y, Liang J et al (2021) A self-adaptive mutation neural architecture search algorithm based on blocks. IEEE Comput Intell Mag 16(3):67–78
Yang X (2008) Introduction to computational mathematics. World Scientific, Singapore
Yang J, Yang T, Zhou C et al (2020) Prediction of critical siltation velocity of slurry pipeline based on improved ABC-LSSVM. J Nanjing Normal Univ (Nat Sci) 43(1):136–142
Yavuz G, Aydin D (2019) Improved Self-adaptive Search Equation-based Artificial Bee Colony Algorithm with competitive local search strategy. Swarm Evol Comput 51:2210–6502
Yu W, Zhan Z, Zhang J (2018) Artificial bee colony algorithm with an adaptive greedy position update strategy. Soft Comput 22:437–451
Yu H, Qiao S, Heidari A et al (2022) Individual disturbance and attraction repulsion strategy enhanced seagull optimization for engineering design. Mathematics 10(2):276
Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173
Acknowledgements
This work is funded by the National Natural Science Foundation of China (Nos. 61862032, 71403112, and 71863018) and Science and Technology Plan Projects of Jiangxi Provincial Education Department (No. GJJ200424).
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Li, X., Zhang, S., Yang, L. et al. Neighborhood-search-based enhanced multi-strategy collaborative artificial Bee colony algorithm for constrained engineering optimization. Soft Comput 27, 13991–14017 (2023). https://doi.org/10.1007/s00500-023-08491-4
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DOI: https://doi.org/10.1007/s00500-023-08491-4