An efficient genetic algorithm for maximizing area coverage in wireless sensor networks

Hanh, Nguyen Thi; Bình, Huỳnh Thị Thanh; Hoài, Nguyễn Xuân; Palaniswami, Marimuthu

doi:10.1016/j.ins.2019.02.059

Cited by 116 publications

(55 citation statements)

References 15 publications

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“…Similarly, PSO and FFA have been applied in model identification of MRE devices [33,48]. Among existing methods, the GA is the most commonly used due to its benefits of easy coding and fast convergence [49,50]. Accordingly, in this study, it is selected to solve the optimization problem for model parameter identification of the MRE device.…”

Section: Modeling Results and Discussionmentioning

confidence: 99%

Comparative Investigation of Phenomenological Modeling for Hysteresis Responses of Magnetorheological Elastomer Devices

et al. 2019

IJMS

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Magnetorheological elastomer (MRE) is a type of magnetic soft material consisting of ferromagnetic particles embedded in a polymeric matrix. MRE-based devices have characteristics of adjustable stiffness and damping properties, and highly nonlinear and hysteretic force–displacement responses that are dependent on external excitations and applied magnetic fields. To effectively implement the devices in mitigating the hazard vibrations of structures, numerically traceable and computationally efficient models should be firstly developed to accurately present the unique behaviors of MREs, including the typical Payne effect and strain stiffening of rubbers etc. In this study, the up-to-date phenomenological models for describing hysteresis response of MRE devices are experimentally investigated. A prototype of MRE isolator is dynamically tested using a shaking table in the laboratory, and the tests are conducted based on displacement control using harmonic inputs with various loading frequencies, amplitudes and applied current levels. Then, the test results are used to identify the parameters of different phenomenological models for model performance evaluation. The procedure of model identification can be considered as solving a global minimization optimization problem, in which the fitness function is the root mean square error between the experimental data and the model prediction. The genetic algorithm (GA) is employed to solve the optimization problem for optimal model parameters due to its advantages of easy coding and fast convergence. Finally, several evaluation indices are adopted to compare the performances of different models, and the result shows that the improved LuGre friction model outperforms other models and has optimal accuracy in predicting the hysteresis response of the MRE device.

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Section: Modeling Results and Discussionmentioning

confidence: 99%

Comparative Investigation of Phenomenological Modeling for Hysteresis Responses of Magnetorheological Elastomer Devices

et al. 2019

IJMS

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“…In KDD Cup 99 data set, there are 41 features that represent the variables used in a computer network [12]. The process of analyzing these all variables is time consuming and requires a large-scale computational steps.…”

Section: Separate Data Set Into Training and Testing Data Setmentioning

confidence: 99%

“…In this paper, intrusion detection technique based on metaheuristic approach known as Genetic Algorithm was developed in order to be applied in a computer network. The method will identify and calculate the differences between the behaviour of the unauthorized connection and normal connection using a proposed fitness (objective) function [12,13]. The proposed technique was executed in two phases; training and testing.…”

Section: Introductionmentioning

confidence: 99%

Network intrusion detection system by using genetic algorithm

Suhaimi

Suliman

Musirin

et al. 2019

IJEECS

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Developing a better intrusion detection systems (IDS) has attracted many researchers in the area of computer network for the past decades. In this paper, Genetic Algorithm (GA) is proposed as a tool that capable to identify harmful type of connections in a computer network. Different features of connection data such as duration and types of connection in network were analyzed to generate a set of classification rule. For this project, standard benchmark dataset known as KDD Cup 99 was investigated and utilized to study the effectiveness of the proposed method on this problem domain. The rules comprise of eight variables that were simulated during the training process to detect any malicious connection that can lead to a network intrusion. With good performance in detecting bad connections, this method can be applied in intrusion detection system to identify attack thus improving the security features of a computer network.

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“…This study primarily focuses on developing a system that can detect the occurrence of unauthorized activity in a computer network and from the intrusion connections detected the type of the attack will be identified. These detection and identification processes were mainly modelled based on metaheuristic approach which is Genetic Algorithm (GA) [5][6]. This algorithm alone is a powerful method in optimization area.…”

Section: Introductionmentioning

confidence: 99%

Network intrusion detection system using immune-genetic algorithm (IGA)

Suhaimi

Suliman

Musirin

et al. 2020

IJEECS

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Network security is an important aspect in maintaining computer network systems and personal information from being illegally accessed by third parties. The major problem that frequently occurs in computer network systems is the failure in detecting possible network-attacks. Apart from that, the process of recognizing the type of attack that occurs is very crucial as it will determine the elimination process that should take place to counter the intrusion. This paper proposes the application of standard Genetic Algorithm (GA) that combines with immune algorithm process to enhance the computer system's capability in recognizing possible intrusion occurrence in a computer system. Simulation was conducted numerous times to test the effectiveness of the proposed intrusion detection system by manipulating the parameter values for genetic operators utilized in GA. The effectiveness of the proposed method is shown in the gathered results and the analysis conducted further supports and proves that Immune Genetic Algorithm (IGA) has the capability to predict the occurrence of intrusion in computer network.

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An efficient genetic algorithm for maximizing area coverage in wireless sensor networks

Cited by 116 publications

References 15 publications

Comparative Investigation of Phenomenological Modeling for Hysteresis Responses of Magnetorheological Elastomer Devices

Comparative Investigation of Phenomenological Modeling for Hysteresis Responses of Magnetorheological Elastomer Devices

Network intrusion detection system by using genetic algorithm

Network intrusion detection system using immune-genetic algorithm (IGA)

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