Abstract
A novel Swarm Intelligence method for best-fit search, Stochastic Diffusion Search, is presented capable of rapid location of the optimal solution in the search space. Population based search mechanisms employed by Swarm Intelligence methods can suffer lack of convergence resulting in ill defined stopping criteria and loss of the best solution. Conversely, as a result of its resource allocation mechanism, the solutions SDS discovers enjoy excellent stability.
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References
Aleksander I, Stonham TJ (1979) Computers & Digitial Techniques 2(1): 29–40
Arthur WB (1994) Amer Econ Rev (Papers and Proceedings) 84: 406
Back T (1996) Evolutionary Algorithms in Theory and Practice. Oxford University Press
Beattie PD, Bishop JM (1998) Journal of Intelligent and Robotic Systems 22: 255–267
Bishop JM (1989) Stochastic Searching Networks. In: IEE Conference Publication No. 313 Proc 1st IEE Int Conf Artificial Neural Networks. London
Bishop JM, Torr PH (1992) The Stochastic Search Network. In: Linggard R, Myers DJ, Nightingale C (eds) Neural Networks for Images, Speech and Natural Language. Chapman Hall, New York
Bishop JM, Nasuto SJ, De Meyer K (2002) Knowledge Representation in Connectionist Systems. In: Dorronsoro JR (ed) Lecture Notes in Computer Science 2415, Springer, Berlin Heidelberg New York
Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm Intelligence: from Natural to Artificial Systems. Oxford University Press, Oxford UK
De Meyer K (2000) Explorations in Stochastic Diffusion Search: soft- and hardware implementations of biologically inspired Spiking Neuron Stochastic Diffusion Networks. Technical Report KDM/JMB/2000-1, University of Reading, UK
De Meyer K, Bishop JM, Nasuto SJ (2002) Small World Network behaviour of Stochastic Diffusion Search. In: Dorronsoro JR (ed) Lecture Notes in Computer Science 2415, Springer, Berlin Heidelberg New York
De Meyer K, Nasuto SJ, Bishop, JM (2006) Stochastic Diffusion Optimisation: the application of partial function evaluation and stochastic recruitment in Swarm Intelligence optimisation, In: Abraham A, Grosam C, Ramos V (eds) Studies in Computational Intelligence (31): Stigmergic Optimization, Springer
Goldberg D (1989) Genetic Algorithms in search, optimization and machine learning. Addison Wesley, Reading MA
Grech-Cini E (1995) Locating facial features. PhD Thesis, University of Reading, Reading UK
Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor
Iosifescu M (1980) Finite Markov processes and their applications. Wiley, Chichester
Kennedy J, Eberhart RC, Shi Y (2001) Swarm Intelligence. Morgan Kauffman, San Francisco
Moglich, M., Maschwitz, U., Holldobler, B., (1974). Science 186 (4168): 1046–1047
Nasuto SJ (1999) Analysis of Resource Allocation of Stochastic Diffusion Search. PhD Thesis, University of Reading, Reading UK
Nasuto SJ, Bishop JM (1999) Journal of Parallel Algorithms and Applications 14: 89–107
Nasuto SJ, Bishop JM, Lauria S (1998) Time Complexity of Stochastic Diffusion Search. In: Heiss M (ed) Proceedings of the International ICSC / IFAC Symposium on Neural Computation. Vienna Austria
Nasuto SJ, Dautenhahn K, Bishop JM (1999) Communication as an Emergent Methaphor for Neuronal Operation. In: Nehaniv C (ed) Lecture Notes in Artificial Intelligence 1562. Springer, New York
Neumaier A (2004) Complete search in continuous global optimization and constraint satisfaction. In: Isereles A (ed) Acta Numerica 2004. Cambridge University Press, Cambridge UK
Whitaker RM, Hurley S (2002) An agent based approach to site selection for wireless networks. In: ACM Press Proc ACM Symposium on Applied Computing. Madrid Spain
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Nasuto, S., Bishop, M. (2008). Stabilizing Swarm Intelligence Search via Positive Feedback Resource Allocation. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78987-1_11
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DOI: https://doi.org/10.1007/978-3-540-78987-1_11
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