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Advances in Bio-inspired Computing for Combinatorial Optimization Problems

ISRL: ADV BIO-INSPRD CMPTNG FOR CMBNTRL OPTMZTN PRBLMS

Information Technology

Computing & Information Technology

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Binding Format

Marketing Imprint Name

B2B GT

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B2B & B2C

'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.

POST SECONDARY FOUR YEAR

COLFR

COLSO

COLJR

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Editorial Board.
Title Page.
Copyright Page.
Dedication.
Preface.
Contents.
1: Biological Computing and Optimization.
2: Bio-inspired Computing.
3: Combinatorial Optimization.
4: Ant Algorithms.
5: Introduction.
6: Local Guided Ant Search.
7: Sensitivity: A Metaheuristic Model.
8: Bio-inspired Multi-agent Systems.
9: Stigmergic Collaborative Agents.
10: Applications with Bio-inspired Algorithms.
11: Ant-Based Algorithms for Dynamic Problems.
12: Agent-Based Algorithms for Diverse Problems.
13: Conclusions and Remarks.
14: Conclusions and the Results Impact.
References.
Index.

Advances in Bio-inspired Computing for Combinatorial Optimization Problems.css-1p8rvzy{position:absolute;padding-left:8px;}

Advances in Bio-inspired Computing for Combinatorial Optimization Problems