Mobile Agent Routing Research Articles

An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem

This paper presents an improved ant colony optimization algorithm (IACO) for solving mobile agent routing problem. The ants cooperate using an indirect form of communication mediated by pheromone trails of scent and find the best solution to their tasks guided by both information ( exploitation) which has been acquired and search ( exploration) of the new route. Therefore the premature convergence probability of the system is lower. The IACO can solve successfully the mobile agent routing problem, and this method has some excellent properties of robustness, self-adaptation, parallelism, and positive feedback process owing to introducing the genetic operator into this algorithm and modifying the global updating rules. The experimental results have demonstrated that IACO has much higher convergence speed than that of genetic algorithm (GA), simulated annealing (SA), and basic ant colony algorithm, and can jump over the region of the local minimum, and escape from the trap of a local minimum successfully and achieve the best solutions. Therefore the quality of the solution is improved, and the whole system robustness is enhanced. The algorithm has been successfully integrated into our simulated humanoid robot system which won the fourth place of RoboCup2008 World Competition. The results of the proposed algorithm are found to be satisfactory.

Autonomous mobile agent routing for efficient server resource allocation

Mobile agents are becoming increasingly important in the highly distributed applications frameworks seen today. Their routing/dispatching from node to node is a very important issue as we need to safeguard application efficiency, achieve better load balancing and resource utilization throughout the underlying network. Selecting the best target server for dispatching a mobile agent is, therefore, a multi-faceted problem that needs to be carefully tackled. In this paper we propose distributed, adaptive routing schemes (next node selection) for mobile agents. The proposed schemes overcome risks like load oscillations, i.e., agents simultaneously abandoning a congested node in search for other, less saturated node. We try to induce different routing decisions taken by agents to achieve load balancing and better utilization of network resources. We consider five different algorithms and evaluate them through simulations. Our findings are quite promising both from the user/application and the network/infrastructure perspective.

On computing mobile agent routes for data fusion in distributed sensor networks

The problem of computing a route for a mobile agent that incrementally fuses the data as it visits the nodes in a distributed sensor network is considered. The order of nodes visited along the route has a significant impact on the quality and cost of fused data, which, in turn, impacts the main objective of the sensor network, such as target classification or tracking. We present a simplified analytical model for a distributed sensor network and formulate the route computation problem in terms of maximizing an objective function, which is directly proportional to the received signal strength and inversely proportional to the path loss and energy consumption. We show this problem to be NP-complete and propose a genetic algorithm to compute an approximate solution by suitably employing a two-level encoding scheme and genetic operators tailored to the objective function. We present simulation results for networks with different node sizes and sensor distributions, which demonstrate the superior performance of our algorithm over two existing heuristics, namely, local closest first and global closest first methods.