Optimization problems can be defined as the problems of making the best possible decision(s) from a set of candidate decisions by utilizing different types of modeling and simulations to support improved choice-making. Optimization is used for many practical problems arising in electronic, civil, chemical, mechanical, and other disciplines of engineering. Many deterministic and none-deterministic algorithms have been proposed for such problems in literatures. The proposed algorithm in this paper, Multi-Battalion Search Algorithm (MBSA), is a heuristic algorithm that simulates the battle field strategies and tactics to find optimal or near optimal solutions for optimization problems. In military aspect, each battalion consists of a specified number of soldiers. One of them is addressed to be the leader (or Colonel) as he represents the most qualified person. The other soldiers should obey and follow his commands. On the other hand, in the MBSA the population is divided into battalions each head by a leader followed by a hierarchy of other ranks. This algorithm solves optimization problems by forcing movement of soldiers in different battalions towards promising areas highlighted by leaders and leadership hierarchy. In addition, it utilizes the power of parallel search represented by the existence of multiple battalions. This algorithm is tested and analyzed against different benchmark problems to check its efficiency for solving optimization problem.
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Agrawal , R., and Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of 20th International Conference on Very Large Data Bases. 487-499.
Banzhaf, W., Nordin, P., Keller, R.E., and Francone, F.D. (1998), Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications, Morgan Kaufmann.
Baucer, A., Bullnheimer, B. , Hartl, R. F. , and Strauss, C. (2000). Minimizing total tardiness on a single machine using ant colony optimization. Central European Journal for Operations Research and Economics, 8, 2, 125-141.
Baxter, P. W., and Possingham, H. P. (2011). Optimizing search strategies for invasive pests: learn before you leap, Journal ofApplied Ecology, 48, 1, 86–95.
Bertsekas, D. P. (1976) Dynamic Programming and Stochastic Control, Academic Press, New York.
Boudarel, R., Delmas, J. and Guichet, P. (1971) Dynamic Programming and its Applications to Optimal Control, Academic Press, New York.
Boyd, S., and Vandenberghe, L. (2004). Convex Optimization, Cambridge University Press New York, NY, USA, section 1.
Carlisle, A. and Dozier. (2000). G. Adapting particle swarm optimization to dynamic environments. Proceedings of International Conference on Artificial Intelligence (ICAI), Las Vegas, USA. 429-434.
Chaoyang, L., (1996). Simulation annealing algorithm with knowledge of imprecision and uncertainty, Systems, Man, and Cybernetics, IEEE International, 3, 1925-1929.
Chakraborty, P., Ghosh Roy, G., Das, S., and Jain, D. (2009). An Improved Harmony Search Algorithm with Differential Mutation Operator, Department of Electronics and Telecommunication Engineering.
Coelho, L. D., and Mariani, V. C. (2009). An improved harmony search algorithm for power economic load dispatch. Energy Conversion and Management, 50. 2522–2526.
Eberhart, R.C., Simpson, P., and Dobbins, R., (1996) Computational Intelligence PC Tools. Academic Press.
Forsyth, D., Champaign, U., Torr, P., and Zisserman, A. (2008). An Experimental Comparison of Discrete and Continuous Shape Optimization Methods,ECCV '08 Proceedings of the 10th European Conference, 332 – 345.
Fraser, Alex S. (1957), Simulation of Genetic Systems by Automatic Digital Computers. I. Introduction.Australian Journal of Biological Sciences, 10. 484-491.
Frost, J. R., 1999, Principles of search theory, Soza & Company.
Fukuyama, Y., Takayama, S., Nakanishi, Y., and Yoshida, H.(1999). A particle swarm optimization for reactive power and voltage control in electric power systems. Proceedings of the Genetic and Evolutionary Computation (GECCO), Florida, USA. 1523-1528.
Gamot, R., and Mesa A. (2008). Particle Swarm Optimization – Tabu Search Approach to Constrained Engineering Optimization Problems, WSEAS Transactions on Mathmatics, 7, 666-675.
Geem, Z. W., Kim, J. H., and Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search, Simulation, Society of Computer Simulation, 76, 60-68.
Glover, F., and McMillan, C. (1986). The general employee scheduling problem: an integration of MS and AI. Computers and Operations Research.
Glover, F. (1989). Tabu Search - Part 1. ORSA Journal on Computing1, 2, 190-206.
Glover, F. (1990). Tabu Search - Part 2. ORSA Journal on Computing 2, 1, 4-32.
Mahdavi, M., Fesanghary, M., and Damangir, E.(2007). An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188, 1567-1579.
Martens, D., De Backer, M., Haesen, R., Vanthienen, J., Snoeck, M., and Baesens, B. (2007). Classification with Ant Colony Optimization, IEEE Transactions on Evolutionary Computation, 11, 5, 651—665.
McCullock, J. (2009). Artificial intelligent. Particle Swarm. http://www.mnemstudio.org/particle-swarm-introduction.htm.
Michalewicz, Z. (1996).Genetic Algorithms + Data Structures = Evolution Programs 3.ed. New York: Springer-Verlag.
Michalewicz, Z., and Fogel, D. B. (2000). How to Solve It, Springer-Verlag Berlin Heidelberg, New York.
Molga, M., Smutnicki, C. (2005). Test functions for optimization needs, http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf
Mu, A., Cao, D., Wang X. (2009). A Modified Particle Swarm Optimization Algorithm, Natural Science, 1,151-155.
Pedersen, M.E.H. 2010. Good parameters for differential evolution. Technical Report HL1002, Hvass Laboratories.
Peng, F., Tang, K., Chen, G., and Yao, X. (2010),Population-based algorithm portfolios for numerical Optimization. Evolutionary computation, IEEE Transactions, 14, 782-800.
Qin, A.K., and Suganthan, P.N. (2005). Self-adaptive differential evolution algorithm for numerical optimization. Proceedings of the IEEE congress on evolutionary computation(CEC). 1785–1791.
Salman, A., Engelbercht, A., and Omran, M. (2007). Empirical analysis of self-adaptive differential evolution, European Journal of Operational Research, 183, 785-804.
Schutte, J. F. and Groenwold, A. A. (2003), Sizing design of truss structures using particle swarms, Structural and Multidisciplinary Optimization, 25. 261-269.
Shi, Y. and Eberhart, R.C., (1999). Empirical study of particle swarm optimization. Proceedings of the 1999 Congress of Evolutionary Computation, 3, 1945–1950.
Singhal, P.K., and Sharma, R.N. (2011). Dynamic programming approach for solving power generating unit commitment problem. Computer and Communication Technology (ICCCT), 2nd International Conference on, 298-303.
Sniedovich, M. (1992). Dynamic Programming, Marcel Dekker, New York.
Storn, R., and Price, K., (1995). Differential Evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report, 95-012. International Computer Science Institute, Berkeley.
Yen, J., Liao, J. C. , Lee, B., and D. Randolph, D. (1998). A Hybrid approach to modeling metabolic systems using genetic algorithms and simplex method. IEEE Transactions on Systems, Man, and Cybernetics. 28, 173-191.
Zhang, C., Shao, H., and Li, Y. (2000) Particle swarm optimisation for evolving artificial neural network. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. 2487-490.