Szczegóły publikacji
Opis bibliograficzny
Efficient constrained evolutionary multi-agent system for multi-objective optimization / Leszek SIWIK, Piotr Sikorski // W: WCCI 2008 [Dokument elektroniczny] : 2008 IEEE World Congress on Computational Intelligence : IJCNN 2008 : FUZZ-IEEE 2008 : CEC 2008 : June 1–6, 2008, Hong Kong : proceedings / IEEE. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE,, 2008. — 1 dysk optyczny. — ( Proceedings of ... International Joint Conference on Neural Networks ; ISSN 2161-4393 ). — Dod. ISBN: 978-1-4244-1819-0. — Congress on Evolutionary Computation ; ISBN: 978-1-4244-1823-7. — e-ISBN: 978-1-4244-1821-3. — S. 3211–3218. — Wymagania systemowe: Adobe Acrobat Reader ; napęd CD-ROM. — Bibliogr. s. 3218, Abstr. — W bazie Web of Science ISBN: 978-1-4244-1822-0 oraz zakres stron: 3212–3219
Autorzy (2)
- AGHSiwik Leszek
- Sikorski Piotr
Dane bibliometryczne
| ID BaDAP | 41069 |
|---|---|
| Data dodania do BaDAP | 2008-10-27 |
| DOI | 10.1109/CEC.2008.4631233 |
| Rok publikacji | 2008 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Czasopismo/seria | Proceedings of ... International Joint Conference on Neural Networks |
Abstract
Evolutionary Multi-agent System approach for optimization (especially for multi-objective optimization) is a very promising computational model. Its computational as well as implemental simplicity causes that approaches based on EMAS model can be widely used for solving optimization tasks. It turns out that introducing some additional mechanisms into basic EMAS-causes that EMAS-based system can be successfully applied for solving constrained multi-objective optimization tasks-and what is important results obtained by proposed approach are better/not worse than results obtained by NSGA-II or SPEA2 algorithms. In the course of this paper some extensions that can be introduced into EMAS in order to constrained multi-objective optimization are presented. What is important-any new additional mechanisms do not have to be introduced into EMAS to solve constrained optimization tasks-the only extensions causing that EMAS-based model becomes an efficient and simple both in conception as well as in implementation-is an appropriate strategy regarding the flow among agents crucial non-renewable resource which is usually called life energy. In this paper, both the idea as well as preliminary results of Constrained Evolutionary Multi-Agent System (conEMAS) for Multi-objective Optimization are presented.
