Author:
Yin Lei,Liu Jin,Fang Yadong,Gao Ming,Li Ming,Zhou Fengyu
Abstract
AbstractRobot cloud service platform is a combination of cloud computing and robotics, providing intelligent cloud services for many robots. However, to select a cloud service that satisfys the robot’s requirements from the massive services with different QoS indicator in the cloud platform is an NP hard problem. In this paper, based on the cost model between the cloud platform, cloud services and cloud service robotics, we propose a two-stage service selection strategy, namely, candidate services selection stage according to the specific QoS requirements of service robots and final cost optimization stage. Additionally, with respect to optimizing the final cost for the model, we propose a Dynamic Vector Hybrid Genetic Algorithm (DVHGA) that is integrated with local and global search process as well as a three-phase parameter updating policy. Specifically, inspired by momentum optimization in deep learning, dynamic vector is integrated with DVHGA to modify the weights of QoS and ensure the reasonable allocation of resources. Moreover, we suggest a linear evaluation method for the service robots and the cloud platform concerning time and final cost at the same time, which could be expected to be used in the real application environment. Finally, the empirical results demonstrate that the proposed DVHGA outperforms other benchmark algorithms, i.e., DABC, ESWOA, GA, PGA and GA-PSO, in convergence rate, total final cost and evaluation score.
Funder
the National Key R & D Program of China
the Jinan ”20 New Colleges and Universities” Funded Scientific Research Leader Studio
the Major Agricultural Applied Technological Innovation Projects of Shandong Province
the Shandong Natural Science Foundation
Beijing Advanced Innovation Center for Intelligent Robots and Systems
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
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