Harmony Search and Cellular Automata in Spatial Optimization


The combined optimization problem of resource production and allocation is considered. The spatial character of the problem is emphasized and cellular modeling is introduced. First a new enhanced harmony search algorithm is applied combined with cellular concepts. Then another new approach is presented involving a cellular automaton combined with harmony search. This second approach renders solutions with greater compactness, a desirable characteristic in spatial optimization. The two algorithms are compared and discussed.

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E. Sidiropoulos, "Harmony Search and Cellular Automata in Spatial Optimization," Applied Mathematics, Vol. 3 No. 10A, 2012, pp. 1532-1537. doi: 10.4236/am.2012.330213.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] E. Sidiropoulos and D. Fotakis, “Cell-Based Genetic Algorithm and Simulated Annealing for Spatial Groundwater Allocation,” WSEAS Transactions on Environment and Development, Vol. 4, No. 5, 2009, pp. 1-10.
[2] E. Sidiropoulos and D. Fotakis, “Spatial Optimization and Resource Allocation in a Cellular Automata Framework,” In: A. Salcido, Ed., Cellular Automata: Simplicity behind Complexity, INTECH Scientific Publishing, Rijeka, 2011.
[3] E. Sidiropoulos and D. Fotakis, “A New Multi-Objective Self-Organizing Optimization Algorithm (MOSOA) for Spatial Optimization Problems,” Applied Mathematics and Computation, Vol. 218, No. 9, 2012, pp. 5168-5180. doi:10.1016/j.amc.2011.11.003
[4] E. Sidiropoulos, “Spatial Resource Allocation via Simulated Annealing on a Cellular Automaton Background,” 2011 World Congress on Engineering and Technology, Shanghai, 28 October-2 November 2011, Vol. 2, p. 137.
[5] E. Sidiropoulos and D. Fotakis, “Spatial Groundwater Allocation via Stochastic and Simulated Evolution on a Cellular Background,” 11th International Conference on Protection and Restoration of the Environment, Thessaloniki, 3-6 July 2012, pp. 34-43.
[6] Z. W. Geem, J. H. Kim and G. V. Loganathan, “A New Heuristic Optimization Algorithm: Harmony Search,” Simulation, Vol. 76, No. 2, 2001, pp. 60-68. doi:10.1177/003754970107600201
[7] K. S. Lee and Z. W. Geem, “A New Meta-Heuristic Algorithm for Continuous Engineering Optimization: Harmony Search Theory and Practice,” Computer Methods in Applied Mechanics and Engineering, Vol. 194, No. 36-38, 2005, pp. 3902-3933. doi:10.1016/j.cma.2004.09.007
[8] M. Mahdavi, M. Fesanghary and E. Damangir, “An Improved Harmony Search Algorithm for Solving Optimization Problems,” Applied Mathematics and Computation, Vol. 188, No. 2, 2007, pp. 1567-1579. doi:10.1016/j.amc.2006.11.033
[9] Q.-K. Pan, P. N. Suganthan, M. F. Tasgetiren and J. J. Liang, “A Self-Adaptive Global Best Harmony Search Algorithm for Continuous Optimization Problems,” Applied Mathematics and Computation, Vol. 216, No. 3, 2010, pp. 830-848. doi:10.1016/j.amc.2010.01.088
[10] M. G. H. Omran and M. Mahdavi, “Global-Best Harmony Search,” Mathematics and Computation, Vol. 198, No. 2, 2008, pp. 643-656.
[11] K. Krishnakumar, “Micro-Genetic Algorithms for Stationary and Non-Stationary Function Optimization,” SPIE Intelligent Control and Adaptive Systems, Philadelphia, Vol. 1196, 1989, pp. 289-296.
[12] P. Vanegas, D. Cattrysse and J. Van Orshoven, “Compactness in Spatial Decision Support: A Literature Review,” Lecture Notes in Computer Science, Springer 2010, pp. 414-429.

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