ABSTRACT:In this paper, the Ant Colony Optimization Technique has been applied in different network models with different number of nodes and structure to find the shortest path with optimum throughput. Three variations of the Ant Colony Optimization Technique, ACO1, ACO2 and ACO3 has been proposed and applied on different standard network models and the results has been analyzed and concluded. A Tabu list is also maintained for a network with large number of nodes and results were collected to find the optimum size of the Tabu list in one of the algorithms proposed here. Experiments have also been performed by varying the load of the network. Here the throughput and the reliability of the network has been specially taken as the performance factor of the network.
Human-robot communication is one of the actively researched fields to enable efficient and seamless collaboration between a human and an intelligent industrial robotic system. The field finds its roots in human communication with the aim to achieve the “naturalness” inherent in the latter. Industrial human-robot communication pursues communication with simplistic commands and gestures, which is not representative of an uncontrolled real-world industrial environment. In addition, naturalness in communication is a consequence of its dynamism, typically ignored as a design criterion in industrial human-robot communication. Complexity Theory-based natural communication models allow for a more accurate representation of human communication which, when adapted, could also benefit the field of human-robot communication. This paper presents a perspective by reviewing the state of human-robot communication in industrial settings and then presents a critical analysis of the same through the lens of Complexity Theory. Furthermore, the work identifies research gaps in the aforementioned field, fulfilling which, would propel the field towards a truly natural form of communication. Finally, the work briefly discusses a general framework that leverages the experiential learning of data-based techniques and naturalness of human knowledge.
This paper suggests new variants of Ant Colony Optimization(ACO)Techniques for Network Routing. There are three existing variants of ACO based on pheromone deposit calculation. In our earlier work we suggested three different heuristics for selecting the next node at each step of iteration. Incorporation of these heuristics in each of the above three variants result into nine variations. In this paper the performance of these nine variations has been studied. Moreover, we have modified the pheromone deposit calculation considering the transmission time of each successful packet(ant) and incorporated this new pheromone update formula in each of the nine variants. As a result, we have obtained nine new variants of ACO. The performance of these new nine variants has been compared with previous ones with respect to the speed of execution, throughput and the number of successful packets. The experiments have been performed over two different network topologies. In one of the variant a tabu list has been incorporated. The length of the tabu list plays a vital role in improving the performance of the routing algorithm. In this paper it has been observed that the new variations of ACO have outperformed the previous ones. These new variants can perform efficient network routing in an environment having variable transmission time along the paths due to congestion or poor link quality.
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