IoT networks 3D deployment using hybrid many-objective optimization algorithms

Mnasri, Sami; Nasri, Nejah; Alrashidi, Malek; Bossche, Adrien van den; Val, Thierry

doi:10.1007/s10732-020-09445-x

Cited by 30 publications

(14 citation statements)

References 36 publications

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“…A widely accepted classification for MOEA considers Pareto-dominance-based, Decomposition-based, and Indicator-based algorithms [34]. MOEAs have been applied in several applications [35,36] that can search for a set of optimal solutions on the Pareto Front. However, it involves much higher-level information, often non-technical, qualitative, and experience-driven, to indicate the final solution with a prohibitive computational cost.…”

Section: Researchmentioning

confidence: 99%

Metaheuristic Algorithms Based on Compromise Programming for Multi-Objective Urban Shipment Problem

Son¹,

Jaafar²,

Aziz³

et al. 2022

Preprint

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The Vehicle Routing Problem (VRP) and its variants are found in many fields, especially logistics. In this study, we introduced an adaptive method to a complex VRP. It combines multi-objective optimization and several forms of VRPs with practical requirements for an urban shipment system. The optimizer needs to consider terrain and traffic conditions. The proposed model also considers customers' expectations, shipper considerations as goals, and the common goal like transportation cost. We offered compromise programming to approach the multi-objective problem by decomposing the original multi-objective problem into a minimized distance-based problem. We designed a hybrid version of the Genetic algorithm with the Local Search algorithm to solve the proposed problem. We evaluate the effectiveness of the proposed algorithm with the Tabu Search algorithm, the original Genetic algorithm on the tested dataset. The results show that our method is an effective decision-making tool for the multi-objective VRP and an effective solver for the new variation of VRP.

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Section: Researchmentioning

confidence: 99%

Metaheuristic Algorithms Based on Compromise Programming for Multi-Objective Urban Shipment Problem

Son¹,

Jaafar²,

Aziz³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Compared with the traditional optimization method, the intelligent optimization method can effectively solve the nonlinear optimization problem, thus the hybrid intelligent optimization algorithm is selected to integrally optimize the layout of the system. For the engineering problem of complex 3D deployment, Nasri et al [33,34] established multi-objective optimization models for the 3D deployment of wireless sensor networks and IoT networks and solved the models with intelligent optimization algorithms. Therefore, it can be inferred that the intelligent optimization algorithm can efficiently solve complex problems.…”

Section: Optimization Algorithmmentioning

confidence: 99%

Integral Layout Optimization of Subsea Production Control System Considering Three-Dimensional Space Constraint

2021

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The subsea production control system, characterized by a complex and diverse structure and high cost, is one of the essential parts of a subsea production system. The rational layout of the subsea production control system is essential to reduce development costs and ensure safe production in offshore fields. Most previous studies on layout design in offshore fields have focused on the oil- and gas-gathering system. However, the layout of the subsea production control system has not thoroughly been researched to date and the seabed terrain and integral optimization have rarely been discussed. This paper focuses on the multi-layer star structure and multi-layer star-tree structure, two common layout structures of subsea production control systems, and establishes the corresponding model with obstacle and seabed terrain conditions. Obtaining the lowest possible total cost was the aim of the model. A hybrid algorithm combining the adaptive mutation particle swarm algorithm and the A-star algorithm was applied to integrally optimize the subsea distribution unit and umbilical touch down point positions, the pipe connection topology and pipe routes. The practicality of this approach is demonstrated by designing a layout with one FPSO and 22 subsea control modules. The results indicate that the multi-layer star-tree layout structure has a lower total cost compared to that of the multi-layer star layout structure. In addition, the results were compared with a case that ignores the seabed terrain, indicating differences in the total construction cost. This method provides engineers with quantitative references and reliable cost estimates to make decisions regarding the layout of the subsea production control system.

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“…This problem has grown and has become a significant difficulty since the minority class is frequently of critical importance, as it represents favorable examples that are rare in nature or expensive to obtain [6]. This is true when considering contexts such as Big Data analytics [7,8,9,10,11,12,13], Biometrics [14,15,16,17,18,19,20,21,22], gene profiling [23], credit card fraud detection [24,25], face image retrieval [24], content-based image retrieval [26,27], disease detection [28,29,30,31,32], internet of things [33,34,35,36,37,38,39,40,41,42,43], Natural Language Processing [44,45], network security [46,47,48,49,50,51,…”

Section: Introductionmentioning

confidence: 99%

Stop Oversampling for Class Imbalance Learning: A Critical Review

Hassanat

Tarawneh

Altarawneh

2022

Preprint

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For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is, models trained on fictitious data may fail spectacularly when put to real-world problems. The fundamental difficulty with oversampling approaches is that, given a real-life population, the synthesized samples may not truly belong to the minority class. As a result, training a classifier on these samples while pretending they represent minority may result in incorrect predictions when the model is used in the real world. We analyzed a large number of oversampling methods in this paper and devised a new oversampling evaluation system based on hiding a number of majority examples and comparing them to those generated by the oversampling process. Based on our evaluation system, we ranked all these methods based on their incorrectly generated examples for comparison. Our experiments using more than 70 oversampling methods and three imbalanced real-world datasets reveal that all oversampling methods studied generate minority samples that are most likely to be majority. Given data and methods in hand, we argue that oversampling in its current forms and methodologies is unreliable for learning from class imbalanced data and should be avoided in real-world applications.

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IoT networks 3D deployment using hybrid many-objective optimization algorithms

Cited by 30 publications

References 36 publications

Metaheuristic Algorithms Based on Compromise Programming for Multi-Objective Urban Shipment Problem

Metaheuristic Algorithms Based on Compromise Programming for Multi-Objective Urban Shipment Problem

Integral Layout Optimization of Subsea Production Control System Considering Three-Dimensional Space Constraint

Stop Oversampling for Class Imbalance Learning: A Critical Review

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