메뉴 건너뛰기




Volumn 233, Issue , 2014, Pages 260-271

On a novel multi-swarm fruit fly optimization algorithm and its application

Author keywords

Cooperative swarms; Fruit fly optimization algorithm (FOA); Multi swarm; Optimization algorithm; Swarm behavior

Indexed keywords


EID: 84894678797     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2014.02.005     Document Type: Article
Times cited : (150)

References (29)
  • 1
    • 77957873490 scopus 로고    scopus 로고
    • A review of nature-inspired algorithms
    • H. Zang, S. Zhang, and K. Hapeshi A review of nature-inspired algorithms J. Bionic Eng. 7 S 2010 S232 S237
    • (2010) J. Bionic Eng. , vol.7 , Issue.S
    • Zang, H.1    Zhang, S.2    Hapeshi, K.3
  • 2
    • 84879491396 scopus 로고    scopus 로고
    • A swarm optimization algorithm inspired in the behavior of the social-spider
    • E. Cuevas, M. Cienfuegos, D. Zaldivar, and M. Perez-Cisneros A swarm optimization algorithm inspired in the behavior of the social-spider Expert Syst. Appl. 40 16 2013 6374 6384
    • (2013) Expert Syst. Appl. , vol.40 , Issue.16 , pp. 6374-6384
    • Cuevas, E.1    Cienfuegos, M.2    Zaldivar, D.3    Perez-Cisneros, M.4
  • 3
    • 0037475094 scopus 로고    scopus 로고
    • The particle swarm optimization algorithm: Convergence analysis and parameter selection
    • I.C. Trelea The particle swarm optimization algorithm: convergence analysis and parameter selection Inf. Process. Lett. 85 6 2003 317 325
    • (2003) Inf. Process. Lett. , vol.85 , Issue.6 , pp. 317-325
    • Trelea, I.C.1
  • 4
    • 3142756516 scopus 로고    scopus 로고
    • Handling multiple objectives with particle swarm optimization
    • C.A.C. Coello, G.T. Pulido, and M.S. Lechuga Handling multiple objectives with particle swarm optimization IEEE Trans. Evol. Comput. 8 3 2004 256 279
    • (2004) IEEE Trans. Evol. Comput. , vol.8 , Issue.3 , pp. 256-279
    • Coello, C.A.C.1    Pulido, G.T.2    Lechuga, M.S.3
  • 6
    • 0942269532 scopus 로고    scopus 로고
    • An ant colony system for permutation flow-shop sequencing
    • K.C. Ying, and C.J. Liao An ant colony system for permutation flow-shop sequencing Comput. Oper. Res. 31 5 2004 791 801
    • (2004) Comput. Oper. Res. , vol.31 , Issue.5 , pp. 791-801
    • Ying, K.C.1    Liao, C.J.2
  • 7
    • 35148821762 scopus 로고    scopus 로고
    • A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm
    • D. Karaboga, and B. Basturk A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm J. Global Optim. 39 3 2007 459 471
    • (2007) J. Global Optim. , vol.39 , Issue.3 , pp. 459-471
    • Karaboga, D.1    Basturk, B.2
  • 8
    • 34548479029 scopus 로고    scopus 로고
    • On the performance of artificial bee colony (ABC) algorithm
    • D. Karaboga, and B. Basturk On the performance of artificial bee colony (ABC) algorithm Appl. Soft Comput. 8 1 2008 687 697
    • (2008) Appl. Soft Comput. , vol.8 , Issue.1 , pp. 687-697
    • Karaboga, D.1    Basturk, B.2
  • 9
    • 79551685695 scopus 로고    scopus 로고
    • Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm
    • W. Shen, X. Guo, C. Wu, and D. Wu Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm Knowledge Based Syst. 24 3 2011 378 385
    • (2011) Knowledge Based Syst. , vol.24 , Issue.3 , pp. 378-385
    • Shen, W.1    Guo, X.2    Wu, C.3    Wu, D.4
  • 10
    • 84855993522 scopus 로고    scopus 로고
    • Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect
    • X.S. Yang, S.S.S. Hosseini, and A.H. Gandomi Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect Appl. Soft Comput. 12 3 2012 1180 1186
    • (2012) Appl. Soft Comput. , vol.12 , Issue.3 , pp. 1180-1186
    • Yang, X.S.1    Hosseini, S.S.S.2    Gandomi, A.H.3
  • 11
    • 27944498182 scopus 로고    scopus 로고
    • Animal search strategies: A quantitative random walk analysis
    • F. Bartumeus, M.G.E. da Luz, G.M. Viswanathan, and J. Catalan Animal search strategies: a quantitative random walk analysis Ecology 86 11 2005 3078 3087
    • (2005) Ecology , vol.86 , Issue.11 , pp. 3078-3087
    • Bartumeus, F.1    Da Luz, M.G.E.2    Viswanathan, G.M.3    Catalan, J.4
  • 12
    • 55849107781 scopus 로고    scopus 로고
    • Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search
    • A.M. Reynolds, and M.A. Frye Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search PLOS ONE 2 4 2007 e354
    • (2007) PLOS ONE , vol.2 , Issue.4 , pp. 354
    • Reynolds, A.M.1    Frye, M.A.2
  • 13
    • 79957831224 scopus 로고    scopus 로고
    • A simulation based fly optimization algorithm for swarms of mini autonomous surface vehicles application
    • Z.Z. Abidin, M.R. Arshad, and U.K. Ngah A simulation based fly optimization algorithm for swarms of mini autonomous surface vehicles application Indian J. Geo-Mar. Sci. 40 2 2011 250 266
    • (2011) Indian J. Geo-Mar. Sci. , vol.40 , Issue.2 , pp. 250-266
    • Abidin, Z.Z.1    Arshad, M.R.2    Ngah, U.K.3
  • 14
    • 84155181068 scopus 로고    scopus 로고
    • A new fruit fly Optimization algorithm: Taking the financial distress model as an example
    • W.T. Pan A new fruit fly Optimization algorithm: taking the financial distress model as an example Knowledge Based Syst. 26 2012 69 74
    • (2012) Knowledge Based Syst. , vol.26 , pp. 69-74
    • Pan, W.T.1
  • 15
    • 84874019736 scopus 로고    scopus 로고
    • Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network
    • S.M. Lin Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network Neural Comput. Appl. 22 3-4 2013 783 791
    • (2013) Neural Comput. Appl. , vol.22 , Issue.34 , pp. 783-791
    • Lin, S.M.1
  • 16
    • 84870024579 scopus 로고    scopus 로고
    • A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm
    • H. Li, S. Guo, C. Li, and J. Sun A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm Knowledge Based Syst. 37 2013 378 387
    • (2013) Knowledge Based Syst. , vol.37 , pp. 378-387
    • Li, H.1    Guo, S.2    Li, C.3    Sun, J.4
  • 17
    • 84896694126 scopus 로고    scopus 로고
    • Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service
    • P.W. Chen, W.Y. Lin, T.H. Huang, and W.T. Pan Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service Appl. Math. Inf. Sci. 7 2 2013 459 465
    • (2013) Appl. Math. Inf. Sci. , vol.7 , Issue.2 , pp. 459-465
    • Chen, P.W.1    Lin, W.Y.2    Huang, T.H.3    Pan, W.T.4
  • 18
    • 84866347982 scopus 로고    scopus 로고
    • A novel modified fly optimization algorithm for designing the self-tuning proportional integral derivative controller
    • C. Li, S. Xu, W. Li, and L. Hu A novel modified fly optimization algorithm for designing the self-tuning proportional integral derivative controller J. Convergence Inf. Technol. 7 16 2012 69 77
    • (2012) J. Convergence Inf. Technol. , vol.7 , Issue.16 , pp. 69-77
    • Li, C.1    Xu, S.2    Li, W.3    Hu, L.4
  • 19
    • 84877835424 scopus 로고    scopus 로고
    • Design and optimization of key control characteristics based on improved fruit fly optimization algorithm
    • Y.F. Xing Design and optimization of key control characteristics based on improved fruit fly optimization algorithm Kybernetes 42 3 2013 466 481
    • (2013) Kybernetes , vol.42 , Issue.3 , pp. 466-481
    • Xing, Y.F.1
  • 20
    • 84887866780 scopus 로고    scopus 로고
    • Using modified fruit fly optimisation algorithm to perform the function test and case studies
    • W.T. Pan Using modified fruit fly optimisation algorithm to perform the function test and case studies Connection Sci. 25 2-3 2013 151 160
    • (2013) Connection Sci. , vol.25 , Issue.23 , pp. 151-160
    • Pan, W.T.1
  • 21
    • 84878440785 scopus 로고    scopus 로고
    • A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem
    • L. Wang, X.L. Zheng, and S.Y. Wang A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem Knowledge Based Syst. 48 2013 17 23
    • (2013) Knowledge Based Syst. , vol.48 , pp. 17-23
    • Wang, L.1    Zheng, X.L.2    Wang, S.Y.3
  • 22
    • 84885639395 scopus 로고    scopus 로고
    • LGMS-FOA: An improved fruit fly optimization algorithm for solving optimization problems
    • D. Shan, G.H. Cao, and H.J. Dong LGMS-FOA: an improved fruit fly optimization algorithm for solving optimization problems Math. Prob. Eng. 37 2013 1 9
    • (2013) Math. Prob. Eng. , vol.37 , pp. 1-9
    • Shan, D.1    Cao, G.H.2    Dong, H.J.3
  • 23
    • 34247096005 scopus 로고    scopus 로고
    • On the efficiency of chaos optimization algorithms for global optimization
    • D.X. Yang, G. Li, and G.D. Cheng On the efficiency of chaos optimization algorithms for global optimization Chaos Solitons Fractals 34 4 2007 1366 1375
    • (2007) Chaos Solitons Fractals , vol.34 , Issue.4 , pp. 1366-1375
    • Yang, D.X.1    Li, G.2    Cheng, G.D.3
  • 24
    • 84870057221 scopus 로고    scopus 로고
    • Improved parallel chaos optimization algorithm
    • X.F. Yuan, Y.M. Yang, and H. Wang Improved parallel chaos optimization algorithm Appl. Math. Comput. 219 8 2012 3590 3599
    • (2012) Appl. Math. Comput. , vol.219 , Issue.8 , pp. 3590-3599
    • Yuan, X.F.1    Yang, Y.M.2    Wang, H.3
  • 25
    • 79551644528 scopus 로고    scopus 로고
    • Particle swarm optimization: Hybridization perspectives and experimental illustrations
    • R. Thangaraj, M. Pant, A. Abraham, and P. Bouvry Particle swarm optimization: hybridization perspectives and experimental illustrations Appl. Math. Comput. 217 12 2011 5208 5226
    • (2011) Appl. Math. Comput. , vol.217 , Issue.12 , pp. 5208-5226
    • Thangaraj, R.1    Pant, M.2    Abraham, A.3    Bouvry, P.4
  • 26
    • 34248143032 scopus 로고    scopus 로고
    • Covariance matrix adaptation for multi-objective optimization
    • C. Igel, N. Hansen, and S. Roth Covariance matrix adaptation for multi-objective optimization Evol. Comput. 15 1 2007 1 28
    • (2007) Evol. Comput. , vol.15 , Issue.1 , pp. 1-28
    • Igel, C.1    Hansen, N.2    Roth, S.3
  • 27
    • 33847199831 scopus 로고    scopus 로고
    • Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems
    • J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems IEEE Trans. Evol. Comput. 10 6 2006 646 657
    • (2006) IEEE Trans. Evol. Comput. , vol.10 , Issue.6 , pp. 646-657
    • Brest, J.1    Greiner, S.2    Boskovic, B.3    Mernik, M.4    Zumer, V.5
  • 28
    • 27144475732 scopus 로고    scopus 로고
    • Self-adaptive differential evolution algorithm for numerical optimization
    • A.K. Qin, P.N. Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, in: Proc. 2005 IEEE Cong. Evol. Comput. 2 (2005) 1785-1791.
    • (2005) Proc. 2005 IEEE Cong. Evol. Comput. , vol.2 , pp. 1785-1791
    • Qin, A.K.1    Suganthan, P.N.2
  • 29
    • 84865362316 scopus 로고    scopus 로고
    • An improved chaos optimization algorithm-based parameter identification of synchronous generator
    • Q. Zhu, X.F. Yuan, and H. Wang An improved chaos optimization algorithm-based parameter identification of synchronous generator Electr. Eng. 94 3 2012 147 153
    • (2012) Electr. Eng. , vol.94 , Issue.3 , pp. 147-153
    • Zhu, Q.1    Yuan, X.F.2    Wang, H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.