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Języki publikacji
Abstrakty
Combinatorial optimization challenges are rooted in real-life problems, continuous optimization problems, discrete optimization problems and other significant problems in telecommunications which include, for example, routing, design of communication networks and load balancing. Load balancing applies to distributed systems and is used for managing web clusters. It allows to forward the load between web servers, using several scheduling algorithms. The main motivation for the study is the fact that combinatorial optimization problems can be solved by applying optimization algorithms. These algorithms include ant colony optimization (ACO), honey bee (HB) and multi-objective optimization (MOO). ACO and HB algorithms are inspired by the foraging behavior of ants and bees which use the process to locate and gather food. However, these two algorithms have been suggested to handle optimization problems with a single-objective. In this context, ACO and HB have to be adjusted to multiobjective optimization problems. This paper provides a summary of the surveyed optimization algorithms and discusses the adaptations of these three algorithms. This is pursued by a detailed analysis and a comparison of three major scheduling techniques mentioned above, as well as three other, new algorithms (resulting from the combination of the aforementioned techniques) used to efficiently handle load balancing issues.
Rocznik
Tom
Strony
3--12
Opis fizyczny
Bibliogr. 38 poz., rys., tab.
Twórcy
autor
- Institute of Applied Computer Science, Lodz University of Technology, Stefanowskiego 18/22, Lodz, Poland
autor
- Institute of Applied Computer Science, Lodz University of Technology, Stefanowskiego 18/22, Lodz, Poland
Bibliografia
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- [36] M. Kashefikia, N. Nematbakhsh, and R. A. Moghadam, „Multiple ant-bee colony optimization for load balancing in packet-switched networks", Int. J. of Comp. Netw. and Commun. (IJCNC), vol. 3, no. 5, pp. 107-117, 2011 (doi: 10.5121/ijcnc.2011.3508).
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- [38] M. B. Jasser, M. Sarmini, and R. Yaseen, „Ant colony optimization (ACO) and a variation of bee colony optimization (BCO) in solving TSP problem, a comparative study", Int. J. of Comp. Appl., vol. 96, no. 9, pp. 1-8, 2014 (doi: 10.5120/16819-6587).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-791f0255-6878-414d-8e87-adbce4306087