Abstract
The role of cellular automata in optimization is a current area of research. This paper presents a multi-objective approach to cellular optimization. A typical nonlinear problem of spatial resource allocation is treated by two alternative methods. The first one is based on a specially designed operative genetic algorithm and the second one on a hybrid annealing – genetic procedure. Pareto front approximations are computed by the two methods and also by a non-cellular version of the second approach. The better performance of the cellular methods is demonstrated and questions for further research are discussed.
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References
Karafyllidis, I.: Design of a dedicated parallel processor for the prediction of forest fire spreading using cellular automata and genetic algorithms. Engineering Applications of Artificial Intelligence 17, 19–36 (2004)
Jennerette, G.D., Wu, J.: Analysis and simulation of land use change in the central Arizona – Phoenix region, USA. Landscape Ecology 16, 611–626 (2001)
Chopard, B., Droz, M., Kolb, M.: Cellular automata approach to non-equilibrium diffusion and gradient percolation. Journal of Physics, A: Mathematical and General 22, 1609–1619 (1989)
Salcido, A.: Equilibrium Properties of the Cellular Automata Models for traffic Flow in a Single Lane. Cellular Automata, Simplicity behind Complexity. In: Salcido, A. (ed.) INTECH (2011)
Sidiropoulos, E., Tolikas, P.: Genetic algorithms and cellular automata in aquifer management. Applied Mathematical Modelling 32(4), 617–640 (2008)
Sidiropoulos, E., Fotakis, D.: Cell-based genetic algorithm and simulated annealing for spatial groundwater allocation. WSEAS Transactions on Environment and Development 4(5) (2009)
Sidiropoulos, E., Fotakis, D.: Spatial optimization and resource allocation in a cellular automata framework. In: Salcido, A. (ed.) Cellular Automata: Simplicity Behind Complexity. INTECH Scientific Publishing (2011)
Sidiropoulos, E.: Cellular automata optimization via evolutionary methods. In: Li, T. (ed.) Cellular Automata. Nova Publishers (2011)
Sidiropoulos, E.: Spatial resource allocation via simulated annealing on a cellular automaton background. In: IEEE Proceedings of the World Congress on Engineering and Technology, October 28 –November. 2, vol. 2, p. 137 (2011)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley (2001)
Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 416–423 (1993)
Smith, K.I., Everson, R.M., Fieldsend, J.E., Murphy, C., Misra, R.: Dominance-Based Multiobjective Simulated Annealing. IEEE Transactions on Evolutionary Computation 12(3) (2008)
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Sidiropoulos, E. (2012). Multi-objective Cellular Automata Optimization. In: Sirakoulis, G.C., Bandini, S. (eds) Cellular Automata. ACRI 2012. Lecture Notes in Computer Science, vol 7495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33350-7_14
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DOI: https://doi.org/10.1007/978-3-642-33350-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33349-1
Online ISBN: 978-3-642-33350-7
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