Topology optimization against cascading failures on wireless sensor networks using a memetic algorithm

Fu, Xiuwen; Pace, Pasquale; Aloi, Gianluca; Yang, Lin; Fortino, Giancarlo

doi:10.1016/j.comnet.2020.107327

Cited by 153 publications

(68 citation statements)

References 34 publications

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“…The proposed memetic GDE3 algorithm combines opposition-based learning, local search, and the differential evolution based strategy to get the benefits of each approach. Memetic algorithms [52] and differential evolution algorithms [53], [54] have been used in past studies for other problems. This study is the first attempt to use a memetic generalized differential evolution algorithm for this problem.…”

Section: B Optimization Techniques For Sequence Design Of Dna Problemmentioning

confidence: 99%

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

2020

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Designing reliable sequences of DNA (Deoxyribonucleic Acid) is a critical task in the fields of DNA computing, and nanotechnology. The quality and reliability of the DNA sequence can directly affect the accuracy of the processing of information stored in sequences. This problem of designing reliable sequences belongs to the NP-hard class of problems. It has many incompatible design criteria, which cannot be optimized at the same time. Many objective evolutionary algorithms can balance conflicting design criteria by using a diverse population of solutions. This paper proposes an opposition-based Memetic Generalized Differential Evolution (MGDE3) to handle four conflicting design criteria for reliable DNA sequence design. Opposition-based learning and local search strategies are suggested to strengthen the explorative and exploitative properties of the proposed MGDE3. The proposed algorithm is bench-marked with small, medium, and large data sets against 7 highly-cited many-objective and multi-objective algorithms. Experimental results and statistical analysis reveal that MGDE3 significantly outperforms the compared algorithms. The proposed method generates reliable real-life sequences of DNA that are substantially better than the DNA sequences generated by other considered algorithms.

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Section: B Optimization Techniques For Sequence Design Of Dna Problemmentioning

confidence: 99%

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

2020

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“…In recent years, new techniques and models have been developed by researchers worldwide [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. During the last 6 years, several studies have been individualized regarding the flash-flood susceptibility investigations, which were carried out through the integration of GIS techniques with bivariate statistical models such as: frequency ratio [ 36 ], weights of evidence [ 37 ], statistical index [ 38 ], evidential belief function [ 39 ], certainty factor [ 40 ], or index of entropy [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Costache

Arabameri

Blaschke

et al. 2021

Sensors

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There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

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“…In this way, some drawbacks of forwarding modeling approaches (e.g., modeling in simulation packages), like large dimensions and inadequacy for occupied spaces [48], have driven engineers to employ artificial intelligence approaches for early estimation of energy [49][50][51]. Artificial intelligence is known as intelligence solutions demonstrated by machines, unlike the natural intelligence provided by animals and humans [52][53][54][55][56][57]. Artificial neural networks (ANN), as one of the most known AI-based solutions, have received increasing attraction recently [58][59][60][61][62].…”

Section: Introductionmentioning

confidence: 99%

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Moayedi¹,

Mosavi²

2021

Preprint

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Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

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Topology optimization against cascading failures on wireless sensor networks using a memetic algorithm

Cited by 153 publications

References 34 publications

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

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