Multiple Ant Colony Optimization for Load Balancing
This paper presents a Multiple Ant Colony Optimization (MACO) approach for load balancing in circuit-switched networks. Based on the problem-solving approach of ants in nature, Ant Colony Optimization (ACO) has been applied to solve problems in optimization, network routing and load balancing by modeling ants as a society of mobile agents. While traditional ACO approaches employed one ant colony for routing, MACO uses multiple ant colonies to search for alternatives to an optimal path. One of the impetuses of MACO is to optimize the performance of a congested network by routing calls via several alternatives paths to prevent possible congestion along an optimal path. Ideas of applying MACO for load-balancing in circuit-switched networks have been implemented in a testbed. Using fairness ratio as a performance measure, experimental results show that MACO is (1) effective in balancing the load, and (2) more effective than traditional ACO for load balancing.
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