Multi Battalion Search Algorithm (MBSA): New optimization search algorithm


  • Abeer Al-Shammari Kuwait University
  • Dr. Kuwait University
  • Prof. Kuwait University


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 choicemaking. 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. 

Author Biographies

Dr., Kuwait University

Computer Engineering Department,

Prof., Kuwait University

Computer Engineering Department,



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.

Avello, E. A., Baesler, F. F., and Moraga, R. J. (2004). A meta-heuristic based on simulated annealing for solving multiple-objective problems in simulation optimization, Simulation Conference.1, 18-27.

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


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 of Applied 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.



How to Cite

Al-Shammari, A., Salman, A. ., & Al-Anzi, F. (2020). Multi Battalion Search Algorithm (MBSA): New optimization search algorithm. International Journal on Information Technology and Computer Science, 6(1). Retrieved from



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