1.

Record Nr.

NYU004617679

Titolo

Agent-based optimization / Ireneusz Czarnowski, Piotr Jędrzejowicz, and Janusz Kacprzyk (eds.).

Pubbl/distr/stampa

Berlin ; New York : Springer, ©2013

ISBN

9783642340970

3642340970

3642340962

9783642340963

Descrizione fisica

1 online resource.

Collana

Studies in computational intelligence, 1860-949X ; 456

Altri autori (Persone)

Czarnowski, Ireneusz

Jędrzejowicz, Piotr

Kacprzyk, Janusz

Disciplina

006.3

Collocazione

Electronic access

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and author index.

Nota di contenuto

Machine Learning and Multiagent Systems as Interrelated Technologies / Ireneusz Czarnowski, Piotr Jędrzejowicz -- Ant Colony Optimization for the Multi-criteria Vehicle Navigation Problem / Mariusz Boryczka, Wojciech Bura -- Solving Instances of the Capacitated Vehicle Routing Problem Using Multi-agent Non-distributed and Distributed Environment / Dariusz Barbucha -- Structure vs. Efficiency of the Cross-Entropy Based Population Learning Algorithm for Discrete-Continuous Scheduling with Continuous Resource Discretisation / Piotr Jędrzejowicz, Aleksander Skakovski -- Triple-Action Agents Solving the MRCPSP/Max Problem / Piotr Jędrzejowicz, Ewa Ratajczak-Ropel -- Team of A-Teams -- A Study of the Cooperation between Program Agents Solving Difficult Optimization Problems / Dariusz Barbucha, Ireneusz Czarnowski, Piotr Jędrzejowicz -- Distributed Bregman-Distance Algorithms for Min-Max Optimization / Kunal Srivastava, Angelia Nedić, Dušan Stipanović -- A Probability Collectives Approach for Multi-Agent Distributed and Cooperative Optimization with Tolerance for Agent Failure / Anand J. Kulkarni, Kang Tai.

Sommario/riassunto

This volume presents a collection of original research works by leading



specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of the agent-based optimization. The book consists of eight chapters covering examples of application of the multi-agent paradigm and respective customized tools to solve difficult optimization problems arising in different areas such as machine learning, scheduling, transportation and, more generally, distributed and cooperative problem solving.