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Erschienen in: Soft Computing 7/2018

17.01.2017 | Methodologies and Application

Modified Gbest-guided artificial bee colony algorithm with new probability model

verfasst von: Laizhong Cui, Kai Zhang, Genghui Li, Xianghua Fu, Zhenkun Wen, Nan Lu, Jian Lu

Erschienen in: Soft Computing | Ausgabe 7/2018

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Abstract

Artificial bee colony (ABC) is a very effective and efficient swarm-based intelligence optimization algorithm, which simulates the collective foraging behavior of the honey bees. However, ABC has strong exploration ability but poor exploitation ability because its solution search equation performs well in exploration but badly in exploitation. In order to enhance the exploitation ability and obtain a better balance between exploitation and exploration, in this paper, a novel search strategy which exploits the valuable information of the current best solution and a novel probability model which makes full use of the other good solutions on onlooker bee phase are proposed. To be specific, in the novel search strategy, a parameter P is used to control which search equation to be used, the original search equation of ABC or the new proposed search equation. The new proposed search equation utilizes the useful information from the current best solution. In the novel probability model, the selected probability of the good solution is absolutely significantly larger than that of the bad solution, which makes sure the good solutions can attract more onlooker bees to search. We put forward a new ABC variant, named MPGABC by combining the novel search strategy and probability model with the basic framework of ABC. Through the comparison of MPGABC and some other state-of-the-art ABC variants on 22 benchmark functions, 22 CEC2011 real-world optimization problems and 28 CEC2013 real-parameter optimization problems, the experimental results show that MPGABC is better than or at least comparable to the competitors on most of benchmark functions and real-world problems.

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Literatur
Zurück zum Zitat Abraham A, Jatoth RK, Rajasekhar A (2012) Hybrid differential artificial bee colony algorithm. J Comput Theor Nanosci 9(2):249–257CrossRef Abraham A, Jatoth RK, Rajasekhar A (2012) Hybrid differential artificial bee colony algorithm. J Comput Theor Nanosci 9(2):249–257CrossRef
Zurück zum Zitat Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192(1):120–142CrossRef Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192(1):120–142CrossRef
Zurück zum Zitat Aydogdu I, Akin A, Saka MP (2016) Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution. Adv Eng Softw 92:1–14CrossRef Aydogdu I, Akin A, Saka MP (2016) Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution. Adv Eng Softw 92:1–14CrossRef
Zurück zum Zitat Banharnsakun A, Achalakul T, Sirrinaovakul B (2011) The best-so-far selection in artificial bee colony algorithm. Appl Soft Comput 11(2):2888–2901CrossRef Banharnsakun A, Achalakul T, Sirrinaovakul B (2011) The best-so-far selection in artificial bee colony algorithm. Appl Soft Comput 11(2):2888–2901CrossRef
Zurück zum Zitat Banharnsakun A, Sirinaovakual B, Achalakul T (2013) The best-so-far ABC with multiple patrilines for clustering problems. Neurocomputing 116:355–366CrossRef Banharnsakun A, Sirinaovakual B, Achalakul T (2013) The best-so-far ABC with multiple patrilines for clustering problems. Neurocomputing 116:355–366CrossRef
Zurück zum Zitat Bayraktar T (2014) A memory-integrated artificial bee algorithm for heuristic optimization, M. SC. thesis. University of Bedfordshire Bayraktar T (2014) A memory-integrated artificial bee algorithm for heuristic optimization, M. SC. thesis. University of Bedfordshire
Zurück zum Zitat Chen SM, Sarosh A, Dong YF (2012) Simulated annealing based artificial bee colony algorithm for global numerical optimization. Appl Math Comput 219(8):3575–3589MathSciNetMATH Chen SM, Sarosh A, Dong YF (2012) Simulated annealing based artificial bee colony algorithm for global numerical optimization. Appl Math Comput 219(8):3575–3589MathSciNetMATH
Zurück zum Zitat Cui Z, Gao X (2012) Theory and applications of swarm intelligence. Neural Comput Appl 21(2):205–206CrossRef Cui Z, Gao X (2012) Theory and applications of swarm intelligence. Neural Comput Appl 21(2):205–206CrossRef
Zurück zum Zitat Das S, Suganthan PN (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur University, India and Nanyang Technological University, Singapore; 2010 Technical report Das S, Suganthan PN (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur University, India and Nanyang Technological University, Singapore; 2010 Technical report
Zurück zum Zitat Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst 26(1):29–41 Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst 26(1):29–41
Zurück zum Zitat Feng JW, Dai AD, Xu C, Wang JY (2011) Designing lag synchronization for unified chaotic systems. Comput Math Appl 61:2123–2128MathSciNetCrossRefMATH Feng JW, Dai AD, Xu C, Wang JY (2011) Designing lag synchronization for unified chaotic systems. Comput Math Appl 61:2123–2128MathSciNetCrossRefMATH
Zurück zum Zitat Fister I, Fjjr I, Brest J, Zumer V (2012) Memetic artificial bee colony algorithm for large-scale global optimization. IEEE Congress on Evolutionary Computation 2012 (pp 1–8). IEEE Fister I, Fjjr I, Brest J, Zumer V (2012) Memetic artificial bee colony algorithm for large-scale global optimization. IEEE Congress on Evolutionary Computation 2012 (pp 1–8). IEEE
Zurück zum Zitat Gao WF, Liu SY (2012) A modified artificial bee colony algorithm. Comput Oper Res 39(3):687–697CrossRefMATH Gao WF, Liu SY (2012) A modified artificial bee colony algorithm. Comput Oper Res 39(3):687–697CrossRefMATH
Zurück zum Zitat Gao WF, Liu SY, Huang LL (2012) A global best artificial bee colony algorithm for global optimization. J Comput Appl Math 236(11):2741–2753MathSciNetCrossRefMATH Gao WF, Liu SY, Huang LL (2012) A global best artificial bee colony algorithm for global optimization. J Comput Appl Math 236(11):2741–2753MathSciNetCrossRefMATH
Zurück zum Zitat Gao WF, Liu SY, Huang LL (2013a) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans Cybern 43(3):1011–1024CrossRef Gao WF, Liu SY, Huang LL (2013a) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans Cybern 43(3):1011–1024CrossRef
Zurück zum Zitat Gao WF, Liu SY, Huang LL (2013b) A novel artificial bee colony algorithm with Powell’s method. Appl Soft Comput 13(9):3763–3775CrossRef Gao WF, Liu SY, Huang LL (2013b) A novel artificial bee colony algorithm with Powell’s method. Appl Soft Comput 13(9):3763–3775CrossRef
Zurück zum Zitat Gao WF, Liu SY, Huang LL (2014) Enhancing artificial bee colony algorithm using more information-based search equations. Inf Sci 270(1):112–133MathSciNetCrossRefMATH Gao WF, Liu SY, Huang LL (2014) Enhancing artificial bee colony algorithm using more information-based search equations. Inf Sci 270(1):112–133MathSciNetCrossRefMATH
Zurück zum Zitat Gao WF, Chan FTS, Huang LL, Liu SY (2015a) Bare bones artificial bee colony algorithm with parameter adaptation and fitness-based neighborhood. Inf Sci 316:180–200CrossRef Gao WF, Chan FTS, Huang LL, Liu SY (2015a) Bare bones artificial bee colony algorithm with parameter adaptation and fitness-based neighborhood. Inf Sci 316:180–200CrossRef
Zurück zum Zitat Gao WF, Huang LL, Liu SY, Chan FTS, Dai C (2015b) Artificial bee colony algorithm with multiple search strategies. Appl Math Comput 271:269–287MathSciNet Gao WF, Huang LL, Liu SY, Chan FTS, Dai C (2015b) Artificial bee colony algorithm with multiple search strategies. Appl Math Comput 271:269–287MathSciNet
Zurück zum Zitat Gao WF, Huang LL, Liu SY, Dai C (2015c) Artificial bee colony algorithm based on information Learning. IEEE Trans Cybern 45(12):2827–2839CrossRef Gao WF, Huang LL, Liu SY, Dai C (2015c) Artificial bee colony algorithm based on information Learning. IEEE Trans Cybern 45(12):2827–2839CrossRef
Zurück zum Zitat Hsieh TJ, Hsiao HF, Yeh WC (2012) Mining financial distress trend data using penalty guided support vector machines based on hybrid of particle swarm optimization and artificial bee colony algorithm. Neurocomputing 82:196–206CrossRef Hsieh TJ, Hsiao HF, Yeh WC (2012) Mining financial distress trend data using penalty guided support vector machines based on hybrid of particle swarm optimization and artificial bee colony algorithm. Neurocomputing 82:196–206CrossRef
Zurück zum Zitat Hu Y, Sim CK, Yang X (2015) A subgradient method based on gradient sampling for solving convex optimization problems. Numer Func Anal Opt 36(12):1559–1584MathSciNetCrossRefMATH Hu Y, Sim CK, Yang X (2015) A subgradient method based on gradient sampling for solving convex optimization problems. Numer Func Anal Opt 36(12):1559–1584MathSciNetCrossRefMATH
Zurück zum Zitat Hu YH, Yu CKW, Li C (2016) Stochastic subgradient method for quasi-convex optimization problems. J Nonlinear Convex Anal 174(4):711–724MathSciNetMATH Hu YH, Yu CKW, Li C (2016) Stochastic subgradient method for quasi-convex optimization problems. J Nonlinear Convex Anal 174(4):711–724MathSciNetMATH
Zurück zum Zitat Hunter A, Chiu KS (2000) Genetic algorithm design of neural network and fuzzy logic controllers. Soft Comput 4(3):186–192CrossRefMATH Hunter A, Chiu KS (2000) Genetic algorithm design of neural network and fuzzy logic controllers. Soft Comput 4(3):186–192CrossRefMATH
Zurück zum Zitat Kang F, Li JJ, Xu Q (2009) Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Comput Struct 87(13–14):816–870 Kang F, Li JJ, Xu Q (2009) Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Comput Struct 87(13–14):816–870
Zurück zum Zitat Kang F, Li JJ, Ma ZY (2011a) Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Inf Sci 181(16):3508–3531MathSciNetCrossRefMATH Kang F, Li JJ, Ma ZY (2011a) Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Inf Sci 181(16):3508–3531MathSciNetCrossRefMATH
Zurück zum Zitat Kang F, Li JJ, Ma ZY, Li H (2011b) Artificial bee colony algorithm with local search for numerical optimization. J Softw 6(3):490–497CrossRef Kang F, Li JJ, Ma ZY, Li H (2011b) Artificial bee colony algorithm with local search for numerical optimization. J Softw 6(3):490–497CrossRef
Zurück zum Zitat Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Erciyes University Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Erciyes University
Zurück zum Zitat Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132MathSciNetMATH Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132MathSciNetMATH
Zurück zum Zitat Karaboga D, Akay B (2011) A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl Soft Comput 11(3):3021–3031CrossRef Karaboga D, Akay B (2011) A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl Soft Comput 11(3):3021–3031CrossRef
Zurück zum Zitat Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471MathSciNetCrossRefMATH Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471MathSciNetCrossRefMATH
Zurück zum Zitat Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697CrossRef Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697CrossRef
Zurück zum Zitat Karaboga D, Gorkemli B (2014) A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Appl Soft Comput 23:227–238CrossRef Karaboga D, Gorkemli B (2014) A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Appl Soft Comput 23:227–238CrossRef
Zurück zum Zitat Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948 Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948
Zurück zum Zitat Kiran MS, Babalik A (2014) Improved artificial bee colony algorithm for continuous optimization problems. J Comput Commun 2:108–116CrossRef Kiran MS, Babalik A (2014) Improved artificial bee colony algorithm for continuous optimization problems. J Comput Commun 2:108–116CrossRef
Zurück zum Zitat Kiran MS, Findik O (2015) A directed artificial bee colony algorithm. Appl Soft Comput 26:454–462CrossRef Kiran MS, Findik O (2015) A directed artificial bee colony algorithm. Appl Soft Comput 26:454–462CrossRef
Zurück zum Zitat Kiran MS, Hakli H, Gunduz M, Uguz H (2015) Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf Sci 300:140–157MathSciNetCrossRef Kiran MS, Hakli H, Gunduz M, Uguz H (2015) Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf Sci 300:140–157MathSciNetCrossRef
Zurück zum Zitat Krink T, Paterlini S (2011) Multiobjective optimization using differential evolution for real-world portfolio optimization. Comput Manag Sci 8(1):157–179MathSciNetCrossRef Krink T, Paterlini S (2011) Multiobjective optimization using differential evolution for real-world portfolio optimization. Comput Manag Sci 8(1):157–179MathSciNetCrossRef
Zurück zum Zitat Kuo RJ, Wang MH, Huang TW (2011) An application of particle swarm optimization algorithm to clustering analysis. Soft Comput 15(3):533–542CrossRef Kuo RJ, Wang MH, Huang TW (2011) An application of particle swarm optimization algorithm to clustering analysis. Soft Comput 15(3):533–542CrossRef
Zurück zum Zitat Li X, Yang GF (2016) Artificial bee colony algorithm with memory. Appl Soft Comput 41:362–372CrossRef Li X, Yang GF (2016) Artificial bee colony algorithm with memory. Appl Soft Comput 41:362–372CrossRef
Zurück zum Zitat Liang JJ, Qu BY, Suganthan PN, Alfredo GH (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Technical report 201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and technical report, Nanyang Technological University, Singapore, January 2013 Liang JJ, Qu BY, Suganthan PN, Alfredo GH (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Technical report 201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and technical report, Nanyang Technological University, Singapore, January 2013
Zurück zum Zitat Lin QZ, Chen JY, Zhan ZH, Chen WN, Coello CAC, Yin YL, Lin CM, Zhang J (2015) A hybrid evolutionary immune algorithm for multiobjective optimization problems. IEEE Trans Evolut Comput 20(5):711–729 Lin QZ, Chen JY, Zhan ZH, Chen WN, Coello CAC, Yin YL, Lin CM, Zhang J (2015) A hybrid evolutionary immune algorithm for multiobjective optimization problems. IEEE Trans Evolut Comput 20(5):711–729
Zurück zum Zitat Loubiere P, Jourdan A, Siarry P, Chelouah R (2016) A sensitivity analysis method for driving the Artificial Bee Colony algorithm’s search process. Appl Soft Comput 41:515–531CrossRef Loubiere P, Jourdan A, Siarry P, Chelouah R (2016) A sensitivity analysis method for driving the Artificial Bee Colony algorithm’s search process. Appl Soft Comput 41:515–531CrossRef
Zurück zum Zitat Luo J, Wang Q, Xiao XH (2013) A modified artificial bee colony algorithm based on converge-onlookers approach for global optimization. Appl Math Comput 219(20):10253–10262MathSciNetMATH Luo J, Wang Q, Xiao XH (2013) A modified artificial bee colony algorithm based on converge-onlookers approach for global optimization. Appl Math Comput 219(20):10253–10262MathSciNetMATH
Zurück zum Zitat Ma M, Liang J, Guo M, Fan Y, Yin YL (2011) SAR image segmentation based on artificial bee colony algorithm. Appl Soft Comput 11(8):5205–5214CrossRef Ma M, Liang J, Guo M, Fan Y, Yin YL (2011) SAR image segmentation based on artificial bee colony algorithm. Appl Soft Comput 11(8):5205–5214CrossRef
Zurück zum Zitat Marinakis Y, Marinaki M, Matsatsinis N (2009) A hybrid discrete artificial bee colony—GRASP algorithm for clustering. In: Proceedings of the international conference on computers & industrial engineering 2009. IEEE, pp 548–553 Marinakis Y, Marinaki M, Matsatsinis N (2009) A hybrid discrete artificial bee colony—GRASP algorithm for clustering. In: Proceedings of the international conference on computers & industrial engineering 2009. IEEE, pp 548–553
Zurück zum Zitat Mavrovouniotis M, Yang SX (2011) A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Comput 15(7):1405–1425CrossRef Mavrovouniotis M, Yang SX (2011) A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Comput 15(7):1405–1425CrossRef
Zurück zum Zitat Omidvar MN, Li XD, Mei Y, Yao X (2014) Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans Evol Comput 18(3):378–393CrossRef Omidvar MN, Li XD, Mei Y, Yao X (2014) Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans Evol Comput 18(3):378–393CrossRef
Zurück zum Zitat Ozturk C, Hancer E, Karaboga D (2015) A novel binary artificial bee colony algorithm based on genetic operators. Inf Sci 297:154–170MathSciNetCrossRef Ozturk C, Hancer E, Karaboga D (2015) A novel binary artificial bee colony algorithm based on genetic operators. Inf Sci 297:154–170MathSciNetCrossRef
Zurück zum Zitat Reza A, Hedayatzadeh R, Ziarati K, Hassanizadeh B (2012) A multi-objective artificial bee colony algorithm. Swarm Evol Comput 2(1):39–52 Reza A, Hedayatzadeh R, Ziarati K, Hassanizadeh B (2012) A multi-objective artificial bee colony algorithm. Swarm Evol Comput 2(1):39–52
Zurück zum Zitat Shalan SAB, Ykhlef M (2015) Multi-objective portfolio optimization problem for Saudi Arabia stock market using hybrid clonal selection and particle swarm optimization. J Sci Eng 40(8):2407–2421 Shalan SAB, Ykhlef M (2015) Multi-objective portfolio optimization problem for Saudi Arabia stock market using hybrid clonal selection and particle swarm optimization. J Sci Eng 40(8):2407–2421
Zurück zum Zitat Shan H, Yasuda T, Ohkura K (2015) A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems. Biosystems 132–133(7):43–53CrossRef Shan H, Yasuda T, Ohkura K (2015) A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems. Biosystems 132–133(7):43–53CrossRef
Zurück zum Zitat Sharma TK, Pant M (2011) Differential operators embedded artificial bee colony algorithm. Int J Appl Evol Comput 2(3):1–14CrossRef Sharma TK, Pant M (2011) Differential operators embedded artificial bee colony algorithm. Int J Appl Evol Comput 2(3):1–14CrossRef
Zurück zum Zitat Shi X, Li Y, Li H, Guan R, Wang L, Liang Y (2010) An integrated algorithm based on artificial bee colony and particle swarm optimization. IEEE Int Conf Neural Netw 5:2586–2590 Shi X, Li Y, Li H, Guan R, Wang L, Liang Y (2010) An integrated algorithm based on artificial bee colony and particle swarm optimization. IEEE Int Conf Neural Netw 5:2586–2590
Zurück zum Zitat Storm R, Price K (1997) Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefMATH Storm R, Price K (1997) Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefMATH
Zurück zum Zitat Sun Y, Zhang CY, Gao L, Wang XJ (2011) Multi-objective optimization algorithms for flow shop scheduling problem: a review and prospects. Int J Adv Mancuf Technol 55(5):723–739CrossRef Sun Y, Zhang CY, Gao L, Wang XJ (2011) Multi-objective optimization algorithms for flow shop scheduling problem: a review and prospects. Int J Adv Mancuf Technol 55(5):723–739CrossRef
Zurück zum Zitat Tang KS, Man KF, Kwong S, He Q (1996) Genetic algorithms and their applications. IEEE Signal Proc Mag 13(6):22–37CrossRef Tang KS, Man KF, Kwong S, He Q (1996) Genetic algorithms and their applications. IEEE Signal Proc Mag 13(6):22–37CrossRef
Zurück zum Zitat Teo J (2006) Exploring dynamic self-adaptive populations in differential evolution. Soft Comput 10(8):673–686CrossRef Teo J (2006) Exploring dynamic self-adaptive populations in differential evolution. Soft Comput 10(8):673–686CrossRef
Zurück zum Zitat Tuba M, Bacanin N (2014) Artificial bee colony algorithm hybridized with firefly algorithm for cardinality constrained mean-variance portfolio selection problem. Appl Math Inf Sci 8(6):2831–2844MathSciNetCrossRef Tuba M, Bacanin N (2014) Artificial bee colony algorithm hybridized with firefly algorithm for cardinality constrained mean-variance portfolio selection problem. Appl Math Inf Sci 8(6):2831–2844MathSciNetCrossRef
Zurück zum Zitat Wang H, Wu Z, Rahnamayan S, Sun H, Liu Y, Pan J (2014) Multi-strategy ensemble artificial bee colony algorithm. Inf Sci 279:587–603MathSciNetCrossRefMATH Wang H, Wu Z, Rahnamayan S, Sun H, Liu Y, Pan J (2014) Multi-strategy ensemble artificial bee colony algorithm. Inf Sci 279:587–603MathSciNetCrossRefMATH
Zurück zum Zitat Wei YH, Xu C, Hu QY (2013) Transformation of optimization problems in revenue management, queueing system, and supply chain management. Int J Prod Econ 146(2):588–597CrossRef Wei YH, Xu C, Hu QY (2013) Transformation of optimization problems in revenue management, queueing system, and supply chain management. Int J Prod Econ 146(2):588–597CrossRef
Zurück zum Zitat Xiang WL, An MQ (2013) An efficient and robust artificial bee colony algorithm for numerical optimization. Comput Oper Res 40(5):1256–1265 Xiang WL, An MQ (2013) An efficient and robust artificial bee colony algorithm for numerical optimization. Comput Oper Res 40(5):1256–1265
Zurück zum Zitat Xiang W, Ma S, An M (2014) hABCDE: a hybrid evolutionary algorithm based on artificial bee colony algorithm and differential evolution. Appl Math Comput 238:370–386MathSciNetMATH Xiang W, Ma S, An M (2014) hABCDE: a hybrid evolutionary algorithm based on artificial bee colony algorithm and differential evolution. Appl Math Comput 238:370–386MathSciNetMATH
Zurück zum Zitat Xiao R, Chen T (2011) Enhancing ABC optimization with Ai-net algorithm for solving project scheduling problem. ICNC 3:1284–1288 Xiao R, Chen T (2011) Enhancing ABC optimization with Ai-net algorithm for solving project scheduling problem. ICNC 3:1284–1288
Zurück zum Zitat Zhang CQ, Zheng JG, Zhou YQ (2015) Two modified artificial bee colony algorithms inspired by grenade explosion method. Neurocomputing 151(3):1198–1207CrossRef Zhang CQ, Zheng JG, Zhou YQ (2015) Two modified artificial bee colony algorithms inspired by grenade explosion method. Neurocomputing 151(3):1198–1207CrossRef
Zurück zum Zitat Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173MathSciNetMATH Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173MathSciNetMATH
Metadaten
Titel
Modified Gbest-guided artificial bee colony algorithm with new probability model
verfasst von
Laizhong Cui
Kai Zhang
Genghui Li
Xianghua Fu
Zhenkun Wen
Nan Lu
Jian Lu
Publikationsdatum
17.01.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 7/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-017-2485-y

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