Abstract
Migrating Birds Optimization (MBO) algorithm is a promising metaheuristic algorithm recently introduced to the optimization community. Despite its superior performance, one drawback of MBO is its occasional aggressive movement to better solutions while searching the solution space. On the other hand, simulated annealing is a well-established metaheuristic optimization method with a search strategy that is particularly designed to avoid getting stuck at local optima. In this study, we present hybridization of the MBO algorithm with the SA algorithm by embedding the exploration strategy of SA into the MBO, which we call Hybrid MBO. In order to investigate impact of this hybridization, we test Hybrid MBO on 100 Quadratic Assignment Problem (QAP) instances taken from the QAPLIB. Our results show that Hybrid MBO algorithm outperforms MBO in about two-thirds of all the test instances, indicating a significant increase in performance.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
