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Branch-and-Bound Bat Algorithm for Solving Complex Optimization Problems

Adeyemo Temitope T., Sanusi Bashir A., Olowoye Adebola O., Olabiyisi Stephen O., Omidiora Elijah O.

Abstract


Most of the real-world problems are concerned with minimizing or maximizing some quantity so as to optimize some outcome. Optimization techniques are used to generate a desired solution to real-life problems. Bat Algorithm (BA) is an evolutionary optimization technique and it is one of the recent metaheuristic algorithms for solving optimization problems. It was inspired by the echolocation behavior of micro-bats, with varying pulse rates of emission and loudness. The major limitation of BA is that it will converge very quickly at an early stage and then convergence rate slow down thereby giving a local optimal solution but not the global optimal solution. Branch-and-bound (BnB) is a common method for improving the searching process by systematically enumerating all candidate solutions and disposing of obviously impossible solutions. This advantage of BnB algorithm will be used to improve BA. BnB was used to improve the best-found solution of BA.

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References


Attia S., Hamdy M., O’Brien W., Carlucci S. Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design. Energy and Buildings. 60, 110-124p, 2013.

Ranut P. Optimization and inverse problems in heat transfer (Doctoral dissertation, Ph. D. thesis, University of Udine). 2012.

Sinha N., Chakrabarti R., Chattopadhyay P.K. Evolutionary programming techniques for economic load dispatch. IEEE Transactions on evolutionary computation. 7(1), 83-94p, 2003.

Roeva O.N., Fidanova S.S. Hybrid bat algorithm for parameter identification of an e. coli cultivation process model. Biotechnology & Biotechnological Equipment. 27(6), 4323-4326p, 2013.

Puchinger J., Raidl G.R. Combining metaheuristics and exact algorithms in combinatorial optimization: A survey and classification. In International Work-Conference on the Interplay Between Natural and Artificial Computation. Springer, Berlin, Heidelberg, 41-53p, 2005.

Portmann M.C., Vignier A., Dardilhac D., Dezalay D. Branch and bound crossed with GA to solve hybrid flowshops. European Journal of Operational Research. 107(2), 389-400p, 1998.

Jourdan L., Basseur M., Talbi E.G. Hybridizing exact methods and metaheuristics: taxonomy. European Journal of Operational Research. 199(3), 620-629p, 2009.

Basseur M., Seynhaeve F., Talbi E.G. Path relinking in pareto multi-objective genetic algorithms. In International Conference on Evolutionary Multi-Criterion Optimization. Springer, Berlin, Heidelberg. 120-134p, 2005, March

Tsai P.W., Pan J.S., Liao B.Y., et al. Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems. Applied Mechanics and Materials. 37(1), 148-149p, 2011. doi: 10.4028/www.scientific.net/AMM.148-149.134, 2011.

Fouad A. Bat Algorithm for Solving Integer Programming Problems. Egyptian Computer Science Journal. 39(1), 25-40p, 2015.


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