02239nam 2200433 450 991031775230332120240112203144.0953-51-5717-5(CKB)4970000000098561(NjHacI)994970000000098561(oapen)https://directory.doabooks.org/handle/20.500.12854/66429(EXLCZ)99497000000009856120221017d2013 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAnt Colony Optimization Techniques and Applications /Helio J. C. Barbosa, editorIntechOpen2013Rijeka, Croatia :IntechOpen,[2013]©20131 online resource (214 pages) illustrations953-51-1001-2 Includes bibliographical references and index.Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented.Ant colony optimizationAntsBehaviorMathematical modelsComputer programming / software developmentAntsBehaviorMathematical models.595.796Barbosa Helio J.Cedt1460884Barbosa Helio J. C.NjHacINjHaclBOOK9910317752303321Ant Colony Optimization3661039UNINA