In this study, the authors propose a multi-objective optimisation-based feature selection (FS) method for the detection of distributed denial of service (DDoS) attacks in an internet of things (IoT) network. An intrus...
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In this study, the authors propose a multi-objective optimisation-based feature selection (FS) method for the detection of distributed denial of service (DDoS) attacks in an internet of things (IoT) network. An intrusion detection system (IDS) is one approach for the detection of cyber-attacks. FS is required to reduce the dimensionality of data and improve the performance of the IDS. One of the reasons for the failure of an IDS is incorrect selection of features because most of the FS methods are based on a limited number of objectives such as accuracy or relevance of data, but these are not enough as they can be misleading for attack detection the contribution of this work is to develop appropriate FS method. They have implemented the nondominated sorting algorithm with its adapted jumping gene operator to solve the optimisation problem and exploited an extreme learning machine as the classifier for FS based on six important objectives for an IoT network. Experimental results verify that the proposed method performs well for FS and have achieved 99.9% and has reduced the total number of features by nearly 90%. The proposed method outperforms other proposed FS methods for the detection of DDoS attacks by an IDS.
Capacitated facility location problems (CFLPs) arise in the practical application of many supply chain networks that select a set of suppliers, plants, distribution centers, and customers. In general, the goal of CFLP...
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Capacitated facility location problems (CFLPs) arise in the practical application of many supply chain networks that select a set of suppliers, plants, distribution centers, and customers. In general, the goal of CFLPs is to consider multiple critical performances that involve quantitative and qualitative factors, such as cost, transportation time, inventory, profit, and customer satisfaction, to obtain various perspectives from decision makers in most real-world applications. CFLP becomes increasingly complex and challenging when decision makers simultaneously consider both factors;however, offering comprehensive decisions is important. In this study, a novel solution based on simplified swarm optimization (SSO) and a nondominatedsorting technique is proposed to provide Pareto-optimal solutions for enhancing search efficiency and solution quality. To yield feasible solutions, three repairer mechanisms, namely, random repair, cost-based, and utility-based mechanisms, are proposed to enhance the search efficiency and diversity of each population. A fuzzy analytic hierarchy process is used to calculate the weight of qualitative objectives. To evaluate the efficiency and effectiveness of the proposed algorithm, extensive experiments are conducted on benchmark and newly generated instances of the four stages of CFLPs. Then, results are compared with those of the nondominatedsorting genetic algorithm-II, multi-objective SSO, and multi-objective particle swarm optimization reported from the literature. The computational results demonstrate that the proposed algorithm is highly competitive and performs well in terms of solution quality and computational time. The Pareto set in the investigated type of facility location problems leads to solutions that may better support decision-making. (C) 2019 Elsevier B.V. All rights reserved.
Energy-efficient train operation is of high importance in urban railways. It takes into account both energy saving and punctuality at the same time, which is the goal of the most operators. In this study, the effect o...
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Energy-efficient train operation is of high importance in urban railways. It takes into account both energy saving and punctuality at the same time, which is the goal of the most operators. In this study, the effect of employing variable regenerative energy recovery rate (RERR) for each inter-station was shown in energy-efficient operation improvement. For this purpose, a two-stage optimisation was proposed. In the first stage, which is a mechanical optimisation, optimal speed profiles for a single train were determined by a bi-objective optimisation through a non-dominated sortingalgorithm. In this stage, a simulation model for determination of train's performance was developed and utilised. It was shown that variable RERRs eventually led to different energy-time Pareto fronts. In the second stage, the total input energy for a multi-train system was minimised by using obtained optimal speed profiles from the first stage and distributing total travelling time among inter-stations (electrical/electromechanical optimisation). It was shown that optimisation of the net energy - with different values of RERR - instead of consumed energy, can reduce the total input energy of the network. The simulation results, which are based on actual operating data of Mashhad urban railway system, confirm the feasibility and effectiveness of the proposed method.
Chloride-induced corrosion is known as the dominant cause of premature damage in reinforced concrete (RC) bridges in the United States. However, the current corrosion management strategies do not suggest an optimum de...
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Chloride-induced corrosion is known as the dominant cause of premature damage in reinforced concrete (RC) bridges in the United States. However, the current corrosion management strategies do not suggest an optimum design procedure for RC bridges in corrosive environments considering both reliability and cost. In this paper, a module based on a reliability-based multiobjective design optimization (RB-MODO) technique using a nondominatedsorting genetic algorithm is proposed for the optimum design of RC bridge beams considering corrosion. The procedure simultaneously maximizes the reliability of the structure and minimizes the material costs, given a design service life. As an illustration, the developed procedure is used for flexural design of interior T beams of a RC bridge with and without considering corrosion effect subjected to various design constraints and service lives. Three types of materials are used in the design process: normal strength concrete with black steel rebars, normal strength concrete with epoxy-coated rebars, and high-performance concrete with black steel rebars. Lastly, the optimum design strategy is selected among the considered materials based on the Pareto front results obtained from the proposed RB-MODO procedure. (C) 2016 American Society of Civil Engineers.
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