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Engineering - Computational Intelligence and Complexity | Advances in Bio-inspired Computing for Combinatorial Optimization Problems

Advances in Bio-inspired Computing for Combinatorial Optimization Problems

Pintea, Camelia-Mihaela

2014, X, 188 p. 45 illus., 3 illus. in color.

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  • 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 for Combinatorial Optimization Problems

"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.

Content Level » Research

Keywords » Artificial Intelligence - Combinatorial Optimization - Intelligent Systems - Metaheuristics - Multi-agent Systems - Natural Computing - Pattern Recognition

Related subjects » Artificial Intelligence - Computational Intelligence and Complexity - Operations Research & Decision Theory

Table of contents 

Part I Biological Computing and Optimization.- Part II Ant Algorithms.- Part III Bio-inspired Multi-Agent Systems.- Part IV Applications with Bio-inspired Algorithms.- Part V Conclusions and Remarks.

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