Advances in Bio-inspired Computing for Combinatorial Optimization Problems, 1st Edition

  • Published By:
  • ISBN-10: 3642401791
  • ISBN-13: 9783642401794
  • DDC: 006.3
  • Grade Level Range: College Freshman - College Senior
  • 188 Pages | eBook
  • Original Copyright 2014 | Published/Released May 2014
  • This publication's content originally published in print form: 2014

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'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.

Table of Contents

Front Cover.
Editorial Board.
Title Page.
Copyright Page.
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.