Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem Ekrem Duman a,⇑ , Mitat Uysal b , Ali Fuat Alkaya c a Ozyegin University, Department of Industrial Engineering, Alemdag, Cekmekoy, Istanbul, Turkey b Dogus University, Department of Computer Engineering, Acibadem, Kadikoy, Istanbul, Turkey c Marmara University, Department of Computer Engineering, Goztepe, Kadikoy, Istanbul, Turkey article info Article history: Received 23 December 2010 Received in revised form 9 June 2012 Accepted 23 June 2012 Available online 2 July 2012 Keywords: Metaheuristics Optimization Birds’ migration V-shape topology Benefit mechanism abstract We propose a new nature inspired metaheuristic approach based on the V flight formation of the migrating birds which is proven to be an effective formation in energy saving. Its per- formance is tested on quadratic assignment problem instances arising from a real life prob- lem and very good results are obtained. The quality of the solutions we report are better than simulated annealing, tabu search, genetic algorithm, scatter search, particle swarm optimization, differential evolution and guided evolutionary simulated annealing approaches. The proposed method is also tested on a number of benchmark problems obtained from the QAPLIB and in most cases it was able to obtain the best known solutions. These results indicate that our new metaheuristic approach could be an important player in metaheuristic based optimization. Ó 2012 Elsevier Inc. All rights reserved. 1. Introduction Solving large scale combinatorial optimization problems optimally is often intractable and one usually has to be content with near optimal solutions. Near optimal solutions are found by heuristic algorithms which can broadly be classified as con- structive and improvement algorithms. Constructive algorithms start from scratch and build a solution gradually whereas improvement algorithms start with a complete solution and try to improve it. Heuristic algorithms are usually developed to solve a specific problem in hand. There is also a class of heuristic algorithms that can be used to solve a large class of prob- lems, either directly or with minor modifications, called metaheuristics [20]. Most metaheuristic algorithms can also be named as neighborhood (or, local) search procedures. These are a wide class of improvement algorithms where at each iteration an improving solution is found by searching the ‘‘neighborhood’’ of the cur- rent solution. A critical issue in the design of a neighborhood search algorithm is the choice of the neighborhood structure, that is, the manner in which the neighborhood is defined [2]. So far many metaheuristics have been proposed by researchers. Among these the genetic algorithm proposed by Holland [24], the simulated annealing proposed by Kirkpatrick et al. [28], the tabu search proposed by Glover [19], the ant colony optimization proposed by Dorigo [11] and the particle swarm optimization proposed by Eberhart and Kennedy [15] are the most popular ones. The harmony search algorithm [18], the artificial bee colony algorithm [27], the monkey search algo- rithm [36], the differential evolution [46] and the firefly algorithm [52] are examples of other competitive metaheuristics proposed recently. Most of these metaheuristics are inspired by nature. This is an indication that although we the mankind are the most intelligent creature in the world, we have lessons to learn from the perfectness of nature. 0020-0255/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ins.2012.06.032 ⇑ Corresponding author. Tel.: +90 216 564 90 00; fax: +90 216 564 90 50. E-mail address: ekrem.duman@ozyegin.edu.tr (E. Duman). Information Sciences 217 (2012) 65–77 Contents lists available at SciVerse ScienceDirect Information Sciences journal homepage: www.elsevier.com/locate/ins