Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking

Rivera, Gilberto; Coello, Carlos A. Coello; Cruz-Reyes, Laura; Fernández, Eduardo; Gómez-Santillán, Claudia; Rangel-Valdez, Nelson

doi:10.1016/j.swevo.2021.101024

Cited by 18 publications

(7 citation statements)

References 74 publications

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“…( 2). This NIDS rate is dependent on level of pheromone and experiential information [25,26]. While N k i is a set of viable regions for the optimal selection of features are not yet visited by ant k, t ij is heuristic function, h ij is amount of pheromone at the edge of i and j by Eq.…”

Section: Selection Of Featurementioning

confidence: 99%

Improved Ant Colony Optimization and Machine Learning Based Ensemble Intrusion Detection Model

Vanitha¹,

Balasubramanie²

2023

Intelligent Automation &Amp; Soft Computing

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Internet of things (IOT) possess cultural, commercial and social effect in life in the future. The nodes which are participating in IOT network are basically attracted by the cyber-attack targets. Attack and identification of anomalies in IoT infrastructure is a growing problem in the IoT domain. Machine Learning Based Ensemble Intrusion Detection (MLEID) method is applied in order to resolve the drawback by minimizing malicious actions in related botnet attacks on Message Queue Telemetry Transport (MQTT) and Hyper-Text Transfer Protocol (HTTP) protocols. The proposed work has two significant contributions which are a selection of features and detection of attacks. New features are chosen from Improved Ant Colony Optimization (IACO) in the feature selection, and then the detection of attacks is carried out based on a combination of their possible properties. The IACO approach is focused on defining the attacker's important features against HTTP and MQTT. In the IACO algorithm, the constant factor is calculated against HTTP and MQTT based on the mean function for each element. Attack detection, the performance of several machine learning models are Distance Decision Tree (DDT), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Mahalanobis Distance Support Vector Machine (MDSVM) were compared with predicting accurate attacks on the IoT network. The outcomes of these classifiers are combined into the ensemble model. The proposed MLEID strategy has effectively established malicious incidents. The UNSW-NB15 dataset is used to test the MLEID technique using data from simulated IoT sensors. Besides, the proposed MLEID technique has a greater detection rate and an inferior rate of false-positive compared to other conventional techniques.

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Section: Selection Of Featurementioning

confidence: 99%

Improved Ant Colony Optimization and Machine Learning Based Ensemble Intrusion Detection Model

Vanitha¹,

Balasubramanie²

2023

Intelligent Automation &Amp; Soft Computing

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“…at is, in the process, only individuals who can reach the target point are selected. In each iteration process, individuals who can reach the target point and have the shortest path are selected to improve the calculation results of ACA [35] gradually. e pheromone update calculation reads…”

Section: Optimizing Acamentioning

confidence: 99%

Application of PID Control and Improved Ant Colony Algorithm in Path Planning of Substation Inspection Robot

Kang

Wang

2022

Mathematical Problems in Engineering

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The purpose is to improve the effect of substation inspection and ensure the safety of power consumption in human society. First, this work discusses the current substation inspection-oriented robot path planning situation. Then, the proportional integration differentiation (PID) control algorithm is introduced and optimized. Ant colony algorithm (ACA) is improved. The substation inspection-oriented RPP model is designed based on the PID algorithm and optimized ACA (the proposed model is denoted as the Ant-PID algorithm). Afterward, the Ant-PID algorithm is compared with the PID control algorithm and ACA. The results show that the longest robot path of the proposed Ant-PID algorithm in different data sets is about 28 m. The shortest is about 26 m, and the number of optimal solutions is maintained at about 45–49. By comparison, the average response time of the PID algorithm is about 25 s to 28 s. The shortest response time of ACA is about 24 s, the shortest average response time is about 27 s, and the longest is about 30 s. The average response time of the proposed ant PID model is about 17 s to 20 s. Therefore, the Ant-PID algorithm can improve the substation inspection robots’ path planning effect. The research results provide technical support for improving the effect of substation inspection and contribute to social power transmission.

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“…After n calculations, each time a layer to be visited is determined, so that a path is formed. A population with m ants, then it is possible to obtain m feasible solutions [ 25 ]. If the ant at the i layer is located close to the location with the highest pheromone content in the current population, then the improved ACO algorithm is performed to transfer the probability formula as follows:

where m i (0) = G ( x ), where G ( x ) is the objective function to be searched for the optimum.…”

Section: Study On the Optimization Model Of The Thickness Of Protecti...mentioning

confidence: 99%

Study on Heat Transfer Characteristics and Intelligent Optimization of Protective Clothing Materials in front of the Blast Furnace

Duan

et al. 2022

Computational Intelligence and Neuroscience

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Special protective clothing should have a certain thermal insulation and protective effect in the production in front of the blast furnace. In order to study the design of special protective clothing for blast furnace foreman, its temperature distribution and internal thickness structure are deeply analyzed. First, based on the existing experimental data, combined with heat transfer and the Fourier law theory, a one-dimensional unsteady heat conduction equation is established. Using the explicit finite difference solution algorithm, the distribution law of the temperature of each fabric layer with time is obtained and the error analysis is performed. The error result is 0.42877%, which shows that the numerical solution is stable and convergent. Then, through the intelligent optimization model of a simulated annealing ant colony (SA-ACO), compared with the single ant colony algorithm (ACO) and a simulated annealing algorithm (SA), it avoids falling into the local optimal solution, reversely solves the optimal thickness of two layers of protective clothing in front of the blast furnace, improves the convergence speed of optimization, and verifies the reliability of the model with a sensitivity test. Finally, this paper actually solves the problem of the protective clothing design and mathematical intelligent model in front of the blast furnace and combines it with numerical simulation software to realize the numerical simulation of temperature distribution and thickness optimization of special protective clothing in front of the blast furnace, so as to provide theoretical guidance for enterprise blast furnace production and development of special protective clothing.

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Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking

Cited by 18 publications

References 74 publications

Improved Ant Colony Optimization and Machine Learning Based Ensemble Intrusion Detection Model

Improved Ant Colony Optimization and Machine Learning Based Ensemble Intrusion Detection Model

Application of PID Control and Improved Ant Colony Algorithm in Path Planning of Substation Inspection Robot

Study on Heat Transfer Characteristics and Intelligent Optimization of Protective Clothing Materials in front of the Blast Furnace

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