Computer Science ›› 2022, Vol. 49 ›› Issue (7): 254-262.doi: 10.11896/jsjkx.210600184

• Computer Network • Previous Articles     Next Articles

Multi-task Cooperative Optimization Algorithm Based on Adaptive Knowledge Transfer andResource Allocation

TANG Feng, FENG Xiang, YU Hui-qun   

  1. School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China
    Shanghai Engineering Research Center of Smart Energy,Shanghai 200237,China
  • Received:2021-06-28 Revised:2021-10-18 Online:2022-07-15 Published:2022-07-12
  • About author:TANG Feng,born in 1997,postgra-duate.Her main research interests include evolutionary computation and swarm intelligence.
    FENG Xiang,born in 1977,Ph.D,professor,is a member of China Computer Federation.Her main research interests include artificial intelligence,swarm intelligence and evolutionary computing,and big data intelligence.
  • Supported by:
    National Natural Science Foundation of China(61772200,61772201,61602175),Shanghai Pujiang Talent Program(17PJ1401900) and Shanghai Economic and Information Commission “Special Fund for Information Development”(201602008).

Abstract: Multi-task optimization algorithm optimizes each task separately and transfers knowledge among tasks at the same time to improve the comprehensive performance of multiple tasks.However,the negative knowledge transfer between tasks with low similarity leads to the overall performance degradation,and allocating the same computing resources to tasks with different difficulties will lead to resource waste.In addition,it is easy to fall into local optimum by using fixed search step size at different stages of the task.To solve these problems,a multi-task collaborative optimization(AMTO) algorithm based on adaptive know-ledge transfer and dynamic resource allocation is proposed.Firstly,each task is optimized by a single population,and a population is divided into three subpopulations.Three different search strategies are adopted to increase the diversity of search behavior.The search step size is dynamically updated according to the individual update success rate in a single task to enhance the adaptive search ability and avoid falling into local optimum.Secondly,the similarity between tasks is calculated online using the feedback results of knowledge transfer among multiple tasks,and the transfer probability is adjusted adaptively according to the similarity.At the same time,when the similarity between tasks is low,the task deviation should be subtracted to reduce the performance degradation caused by negative knowledge transfer and improve the perception ability of the algorithm to the differences between tasks.Then,the difficulty and optimization state of the task is estimated by evaluating the improvement degree of the task performance,and the resources are dynamically allocated on demand for the tasks with different difficulties and states to maximize the utilization value of resources and reduce the waste of resources.Finally,on the simple and the complex multi-task optimization functions,the proposed algorithm is compared with the classical multi-task algorithms to verify the effectiveness of the adaptive migration strategy,dynamic resource allocation strategy and synthesis.

Key words: Adaptive knowledge transfer, Computing resource allocation, Dynamic group search algorithm, Inter-task deviation, Multi-task collaborative optimization

CLC Number: 

  • TP183
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