Migrating Birds Optimization: A New Meta-heuristic Approach and Its Application to the Quadratic Assignment Problem

In this study we propose a new nature inspired metaheuristic approach based on the V formation flight of the migrating birds which is proven to be an effective formation in energy minimization. Its performance is tested on quadratic assignment problem instances arising from a real life problem and very good results are obtained. The quality of the solutions turned out to be better than simulated annealing, tabu search and guided evolutionary simulated annealing approaches. These results indicate that our new metaheuristic approach could be an important player in metaheuristic based optimization.

[1]  D. Hummel,et al.  Aerodynamische Interferenzeffekte beim Formationsflug von Vögeln , 2005, Journal für Ornithologie.

[2]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[3]  Frank H. Heppner,et al.  THE VEE FORMATION OF CANADA GEESE , 1974 .

[4]  Jin-Kao Hao,et al.  Using solution properties within an enumerative search to solve a sports league scheduling problem , 2008, Discret. Appl. Math..

[5]  Hsinchun Chen,et al.  Intelligent internet searching agent based on hybrid simulated annealing , 2000, Decis. Support Syst..

[6]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[7]  F. Hainsworth,et al.  Energy savings through formation flight? A re-examination of the vee formation , 1981 .

[8]  Ronaldo Menezes,et al.  A bio-inspired crime simulation model , 2009, Decis. Support Syst..

[9]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[10]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[11]  Mitat Uysal Using heuristic search algorithms for predicting the effort of software projects , 2009 .

[12]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[13]  Kaoru Hirota,et al.  Hyperbox clustering with Ant Colony Optimization (HACO) method and its application to medical risk profile recognition , 2009, Appl. Soft Comput..

[14]  J K Hedrick,et al.  A systems interpretation for observations of bird V-formations. , 2003, Journal of theoretical biology.

[15]  A. Mucherino,et al.  Monkey search: a novel metaheuristic search for global optimization , 2007 .

[16]  Cutts,et al.  ENERGY SAVINGS IN FORMATION FLIGHT OF PINK-FOOTED GEESE , 1994, The Journal of experimental biology.

[17]  Magdalene Marinaki,et al.  Honey Bees Mating Optimization algorithm for financial classification problems , 2010, Appl. Soft Comput..

[18]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[19]  William A. Miller,et al.  An evolutionary algorithm-based decision support system for managing flexible manufacturing , 2004 .

[20]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[21]  Nashat Mansour,et al.  A genetic algorithm approach for regrouping service sites , 2004, Comput. Oper. Res..

[22]  F. Hainsworth Precision and Dynamics of Positioning by Canada Geese Flying in Formation , 1987 .

[23]  P. Charbonneau Genetic algorithms in astronomy and astrophysics , 1995 .

[24]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[25]  J. Rayner A New Approach to Animal Flight Mechanics , 1979 .

[26]  Bing Sun,et al.  Numerical solution to the optimal feedback control of continuous casting process , 2007, J. Glob. Optim..

[27]  Ekrem Duman,et al.  The quadratic assignment problem in the context of the printed circuit board assembly process , 2007, Comput. Oper. Res..

[28]  María Jesús Álvarez,et al.  Routing design for less-than-truckload motor carriers using Ant Colony Optimization , 2010 .

[29]  M. Andersson,et al.  Kin selection and reciprocity in flight formation , 2004 .

[30]  Abraham P. Punnen,et al.  A survey of very large-scale neighborhood search techniques , 2002, Discret. Appl. Math..

[31]  P. Lissaman,et al.  Formation Flight of Birds , 1970, Science.

[32]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[33]  M. Tamer Ayvaz,et al.  Application of Harmony Search algorithm to the solution of groundwater management models , 2009 .

[34]  Bin Jiao,et al.  A similar particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan , 2006, Appl. Math. Comput..