Improved Quantum Chaotic Animal Migration Optimization Algorithm for QoS Multicast Routing Problem
| Autor: | Mohammed Mahseur, Yassine Meraihi, Abdelmadjid Boukra |
|---|---|
| Přispěvatelé: | Department of Informatics [Alger], Université des Sciences et de la Technologie Houari Boumediene [Alger] (USTHB), Automation Department [Boumerdes], Université M'Hamed Bougara Boumerdes (UMBB), Abdelmalek Amine, Malek Mouhoub, Otmane Ait Mohamed, Bachir Djebbar, TC 5 |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
021103 operations research
Multicast routing Multicast Computer science Distributed computing Quality of service 0211 other engineering and technologies Chaotic Quantum representation 02 engineering and technology Animal migration optimization Quality of Service (QoS) Chaotic maps 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Routing (electronic design automation) Jitter |
| Zdroj: | IFIP Advances in Information and Communication Technology 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA) 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. pp.128-139, ⟨10.1007/978-3-319-89743-1_12⟩ Computational Intelligence and Its Applications ISBN: 9783319897424 CIIA |
| Popis: | Part 2: Evolutionary Computation; International audience; In recent years, we are witnessing the spread of many and various modern real-time applications implemented on computer networks such as video conferencing, distance education, online games, and video streaming. These applications require the high quality of different network resources such as bandwidth, delay, jitter, and packet loss rate. In this paper, we propose an improved quantum chaotic animal migration optimization algorithm to solve the multicast routing problem (Multi-Constrained Least Cost MCLC). We used a quantum representation of the solutions that allow the use of the original AMO version without discretization, as well as improving AMO by introducing chaotic map to determine the random numbers. These two contributions improve the diversification and intensification of the algorithm. The simulation results show that our proposed algorithm has a good scalability and efficiency compared with other existing algorithms in the literature. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|