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RISS 인기검색어
- 검색키워드
- 저자 : Nima Jafari Navimipour (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Nima Jafari Navimipour,Saied Asghari 대한전자공학회 2017 대한전자공학회 학술대회 Vol.2017 No.1
In the past few years, with the extension of distribution systems such as the Internet and web services, grid systems emerged to be one of the utilizable solutions to response the huge computing needs. Grid systems offer a vast amount of calculating resources from multiple administrative domains to reach the main goal. One of the main challenges in these systems is to choose the appropriate services for combination. According to the service-oriented architecture paradigm, the composite service consists of a set of abstract tasks interconnected by various workflow constructs. Consumed energy in these systems is an important issue; however, in many of the previous methods, it is not considered. Therefore, this paper proposes a new service composition mechanism based on an ant colony optimization algorithm (ACO) to reduce the consumed energy. The ACO algorithm is a famous meta-heuristic algorithm where ants try to select the best node for moving in each iteration. Java programming language is used to evaluate the proposed method in terms of the consumed energy and execution time. The obtained results showed better performance of the proposed algorithm in comparison with the greedy algorithm.
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Negar Dordaie,Nima Jafari Navimipour 한국통신학회 2018 ICT Express Vol.4 No.4
Task scheduling is one of the most important issues in heterogeneous environments when high efficiency is required. Because task scheduling is a Nondeterministic Polynomial (NP)-hard problem, many evolutionary algorithms have been adopted to solve this problem. Since the convergence speed of solutions in population-based algorithms is low, they are integrated with local search algorithms. Thus, in this paper, to optimize the task scheduling makespan, a hybrid particle swarm optimization and hill climbing algorithm is proposed. The experimental results on random and scientific Directed Acyclic Graph (DAG) showed that the proposed algorithm performs effectively in terms of the makespan compared to the current well-known heuristic and particle swarm optimization algorithms.
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Bahram Hajimirzaei,Nima Jafari Navimipour 한국통신학회 2019 ICT Express Vol.5 No.1
This paper proposes a new intrusion detection system (IDS) based on a combination of a multilayer perceptron (MLP) network, and artificial bee colony (ABC) and fuzzy clustering algorithms. Normal and abnormal network traffic packets are identified by the MLP, while the MLP training is done by the ABC algorithm through optimizing the values of linkage weights and biases. The CloudSim simulator and NSL-KDD dataset are used to verify the proposed method. Mean absolute error (MAE), root mean square error (RMSE), and the kappa statistic are considered as evaluation criteria. The obtained results have indicated the superiority of the proposed method in comparison with state-of-the-art methods.
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