{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:28:17Z","timestamp":1747189697376,"version":"3.40.5"},"reference-count":45,"publisher":"World Scientific Pub Co Pte Ltd","issue":"16","funder":[{"name":"the Ministerial Project in Fields-of-Interest","award":["61400010205"],"award-info":[{"award-number":["61400010205"]}]},{"name":"the Ministerial Pre-Research Project","award":["41401030301"],"award-info":[{"award-number":["41401030301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2020,12,30]]},"abstract":"<jats:p> In mobile cloud computing, the computing resources of mobile devices can be integrated to execute complicated applications, in order to tackle the problem of insufficient resources of mobile devices. Such applications are, in general, characterized as workflows. Scheduling workflow tasks on a mobile cloud system consisting of heterogeneous mobile devices is a NP-hard problem. In this paper, intelligent algorithms, e.g., particle swarm optimization (PSO) and simulated annealing (SA), are widely used to solve this problem. However, both PSO and SA suffer from the limitation of easily being trapped into local optima. Since these methods rely on their evolutionary mechanisms to explore new solutions in solution space, the search procedure converges once getting stuck in local optima. To address this limitation, in this paper, we propose two effective metaheuristic algorithms that incorporate the iterated local search (ILS) strategy into PSO and SA algorithms, respectively. In case that the intelligent algorithm converges to a local optimum, the proposed algorithms use a perturbation operator to explore new solutions and use the newly explored solutions to start a new round of evolution in the solution space. This procedure is iterated until no better solutions can be explored. Experimental results show that by incorporating the ILS strategy, our proposed algorithms outperform PSO and SA in reducing workflow makespans. In addition, the perturbation operator is beneficial for improving the effectiveness of scheduling algorithms in exploring high-quality scheduling solutions. <\/jats:p>","DOI":"10.1142\/s0218126620502552","type":"journal-article","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T16:22:58Z","timestamp":1592497378000},"page":"2050255","source":"Crossref","is-referenced-by-count":2,"title":["Effective Algorithms for Scheduling Workflow Tasks on Mobile Clouds"],"prefix":"10.1142","volume":"29","author":[{"given":"Heng","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing, Jiangsu 210094, P. R. 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