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In this paper, we formulated the optimal minimization of makespan and energy consumption in task scheduling using Local Pollination-based Gray Wolf Optimizer (LPGWO) algorithm. In the hybrid concept, Gray Wolf Optimizer (GWO) algorithm and Flower Pollination Algorithm (FPA) are combined and used. In the presence of GWO, the best searching factor is used to increase the convergence speed and FPA is used to distribute the data to the next packet of candidate solution using local pollination concept. Chaotic mapping and OBL are used to provide a suitable initial candidate for task solutions. Therefore, the experiments delivered better task scheduling results in low and high heterogeneities of physical machines. Ultimately, the comparison with the simulation results had shown the minimum convergence speed of makespan and energy consumption. <\/jats:p>","DOI":"10.1142\/s0218126620501005","type":"journal-article","created":{"date-parts":[[2019,7,29]],"date-time":"2019-07-29T08:21:33Z","timestamp":1564388493000},"page":"2050100","source":"Crossref","is-referenced-by-count":18,"title":["Multi-Objective Local Pollination-Based Gray Wolf Optimizer for Task Scheduling Heterogeneous Cloud Environment"],"prefix":"10.1142","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0045-6556","authenticated-orcid":false,"given":"M.","family":"Gokuldhev","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita College of Engineering and Technology, Amrita Institutions, Erachakulam, Nagarcoil 629901, Kanyakumari, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Singaravel","sequence":"additional","affiliation":[{"name":"Department of Information Technology, K.S.R. 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