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Titlebook: Advances in Bio-inspired Computing for Combinatorial Optimization Problems; Camelia-Mihaela Pintea Book 2014 Springer-Verlag Berlin Heidel

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期刊全稱Advances in Bio-inspired Computing for Combinatorial Optimization Problems
影響因子2023Camelia-Mihaela Pintea
視頻videohttp://file.papertrans.cn/147/146858/146858.mp4
發(fā)行地址Introduces new bio-inspired techniques based on ants, agents and virtual robots.Solves real-life complex problems using the introduced bio-inspired techniques.Recent research on Bio-inspired Computing
學科分類Intelligent Systems Reference Library
圖書封面Titlebook: Advances in Bio-inspired Computing for Combinatorial Optimization Problems;  Camelia-Mihaela Pintea Book 2014 Springer-Verlag Berlin Heidel
影響因子."Advances in Bio-inspired Combinatorial Optimization Problems" illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems..Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed..Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive ants; virtual sensitive robots; ant-based techniques for static and dynamic routing problems; stigmergic collaborative agents and learning sensitive agents..This monograph is useful for researchers, students and all people interested in the recent natural computing frameworks. The reader is presumed to have knowledge of combinatorial optimization, graph theory, algorithms and programming. The book should furthermore allow readers to acquire ideas, concepts and models to use and develop new software for solving complex real-life problems..
Pindex Book 2014
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Broadband traffic characteristics,oraging behavioral studies of ants colonies had revealed that they can travel between their nests and food sources in a highly efficient way. Based on these kind of observations, the ant system introduced in Dorigo’s PhD Thesis [72] was one of the first bio-inspired models for solving optimization problems.
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U.S. Energy Ecosystem: Entering a New Era?ul method to efficiently address .-hard problems. The agents that form a system inter-operate to produce a solution using both direct and indirect (stigmergic) communication. In this way, intelligent problem solutions can naturally emerge due to stigmergy (see section 3.1).
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