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Hybrid Elephant Herding Optimization Approach for Cloud Computing Load Scheduling 2020 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/9873 https://link.springer.com/chapter/10.1007/978-3-030-37838-7_18 I. Strumberger E. Tuba N. Bacanin M. Tuba Cloud computing is rather important distributing computing paradigm and in general refers to the common pool of configurable resources that is accessed on-demand. Resources are dynamically scalable and metered with the basic aim to provide reliable and quality services to the end-users. Load scheduling has a great impact on the overall performance of the cloud system, and at the same time it is one of the most challenging problems in this domain. In this paper, we propose implementation of the hybridized elephant herding optimization applied to load scheduling problem in cloud computing. The algorithm is using CloudSim framework, and comparison with different metaheuristics, adapted and tested under same experimental conditions, for this type of problem was performed. Moreover, we compared proposed hybridized elephant herding optimization with its original version in order to evaluate its improvements in performance over the original version. Obtained empirical results prove the robustness and quality of approach that we propose in this paper. bookPart Springer 201 212 https://doi.org/10.1007/978-3-030-37838-7_18 Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing, SEMCCO 2019, FANCCO 2019, Communications in Computer and Information Science, vol 1092.