Name
Title | ||
|---|---|---|
Hybrid-TC: A Hybrid Teaching-Learning-Based Optimization Algorithm for Service Composition in Cloud Manufacturing |
Abstract | ||
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During the process of cloud manufacturing, service composition is an essential technology to ensure the smooth execution of the task. An important challenge for cloud manufacturing is how to implement effective and accurate service composition strategy, which are to execute the task effectively while also satisfy the requirements of user by maximizing the overall quality of service (QoS). However, the current cloud manufacturing service composition methods generally have the problem of poor solution quality in the large scale environment. To solve this problem, we propose a hybrid optimization algorithm, named hybrid-TC, which is a hybrid of the teaching-learningbased optimization algorithm (TLBO) and the crisscross optimization algorithm (CSO). First, we added Skyline query in the initialization phase to improve the convergence speed and the quality of the solution. Second, the horizontal crossover of CSO is used in the teaching phase of original TLBO, thereby some dimensions in the population that trapped in the local optimum have the chance to jump out of the iteration. Finally, the offspring individuals learned in the teaching-phase will continue to learn to improve the quality of the solution. Experiments show that our proposed method is effective for improving the solution quality of large-scale environmental service composition. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9533977 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
cloud manufacturing, service composition, quality of service, hybrid teaching learning based optimization | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||