A Modified GSO Based on Limited Storage Quasi-Newton Method
Abstract
Group search optimizer is a new populationbased swarm intelligent algorithm inspired by the animal searching behavior. However, the exploitation capability is not very well. In this paper, the Limited Storage Quasi-Newton Method is incorporated into group search optimizer (GSO) to increase the local search capability. To test the performance, we apply it to solve non-linear equations.Simulation results show it is effective.
Keywords
References
[1] C. Y. Lee and X. Yao. (Feb. 2004). Evolutionary programming using mutations based on the l’evy probability distribution. Evolutionary Computation, IEEE Transactions on, 8(1):1–13.
[2] S. He. “Training artificial neural networks using l’evy group search optimizer”. 20 pages. Submitted to Journal of Multiple-Valued Logic and Soft Computing.
[3] Q. H. Wu, S. He and J. R. Saunders. "An improved group search optimizer for security-constrained optimal power flow". Submitted to IEEE Transactions on Power Systems.
[4] S. He, Q. H. Wu, and J. R. Saunders. "A group search optimizer for neural network training", 2006 International Conference on Computational Science and its Applications (ICCSA 2006). Glasgow. May 2006. Lecture Notes in Computer Science, 3982: 934-943,
[5] Barnard, C.J., Sibly, R.M.: Producers and scroungers: a general model and itsapplication to captive flocks of house sparrows. Animal Behaviour 29 (1981) 543–550
http://dx.doi.org/10.1016/S0003-3472(81)80117-0
[6] Couzin, I., Krause, J., Franks, N., Levin, S.: Effective leadership and decision-making in animal groups on the move. Nature 434 (2005) 513–516
http://dx.doi.org/10.1038/nature03236
PMid:15690039
[7] Bell, J.W.: Searching Behaviour - The Behavioural Ecology of Finding Resources.
[8] Chapman and Hall Animal Behaviour Series. Chapman and Hall (1990)
[9] O’Brien, W.J., Evans, B.I., Howick, G.L.: A new view of the predation cycle of a planktivorous fish, white crappie (pomoxis annularis). Can. J. Fish. Aquat. Sci. 43 (1986) 1894–1899
http://dx.doi.org/10.1139/f86-234
[10] Harper, D.G.C.: Competitive foraging in mallards: ‘ideal free’ ducks. Animal Behaviour 30 (1988) 575–584
http://dx.doi.org/10.1016/S0003-3472(82)80071-7
[11] Dusenbery, D.B.: Ranging strategies. Journal of Theoretical Biology 136 (1989) 309–316
http://dx.doi.org/10.1016/S0022-5193(89)80166-3
[12] Viswanathan, G.M., Buldyrev, S.V., Havlin, S., da Luz, M.G., Raposo, E., Stanley, H.E.: Optimizing the success of random searches. Nature 401(911-914) (1999)
[13] Dixon, A.F.G.: An experimental study of the searching behaviour of the predatorycoccinellid beetle adalia decempunctata. J. Anim. Ecol. 28 (1959) 259–281
http://dx.doi.org/10.2307/2082
[14] S. He, Q. H. Wu, and J. R. Saunders. A group search optimizer for neural network training, 2006 International Conference on Computational Science and its Applications (ICCSA 2006). Glasgow. May 2006. Lecture Notes in Computer Science, 3982: 934-943, Springer.
Full Text: PDF


