2011/10/11 by Mehdi Kashefikia, Kashefikia, Mehdi, Nasser Nematbakhsh +3
Computer Science · Engineering · #Advanced Optical Network Technologies #Ant colony #Ant colony optimization algorithms #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer network #Computer science #Distributed computing #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #Load balancing (electrical power) #Machine learning #Network Traffic and Congestion Control #Network packet #Networking and Internet Architecture (cs.NI) #Particle swarm optimization #Routing (electronic design automation) #Swarm behaviour #Swarm intelligence #cs.AI #cs.NI
paper · pdf · doi:10.48550/arxiv.1110.2341
published in arXiv (Cornell University) (Cornell University) · This paper has been withdrawn by the author
openalex publication_date 2011/10/11 · arxiv created 2011/10/26 · arxiv updated 2011/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
One of the important issues in computer networks is "Load Balancing" which leads to efficient use of the network resources. To achieve a balanced network it is necessary to find different routes between the source and destination. In the current paper we propose a new approach to find different routes using swarm intelligence techniques and multi colony algorithms. In the proposed algorithm that is an improved version of MACO algorithm, we use different colonies of ants and bees and appoint these colony members as intelligent agents to monitor the network and update the routing information. The survey includes comparison and critiques of MACO. The simulation results show a tangible improvement in the aforementioned approach.