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Subject: search.filters.subject.Genetic algorithms

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    Fuzzy constraint prioritization to solve heavily constrained problems with the genetic algorithm
    (Elsevier, 2023) Alouane, Basma; Boulif, Menouar
    Genetic algorithms (GAs) are approximate solving methods that have been originally proposed to achieve unconstrained optimization. To handle constrained problems, which is the case for the majority of real-life circumstances, GAs must be equipped with a constraint-handling mechanism. Transformation functions (TFs) are among the constraint-handling approaches that intervene in the phenotypic space. In this paper, we study the impact of considering constraint priorities on the GA performance when it deals with heavily constrained problems. Priorities are set by integrating a constraint order into the TF definition. We consider different TF forms enhanced with a fuzzy inference engine to find the best constraint ordering. Finally, we conduct an experimental study to assess the performance of the proposed approach on the semi-supervised graph partitioning problem. The obtained results show with statistical evidence that the proposed fuzzy method is promising
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    Optimal Placement of Fault Passage Indicators in Distribution Networks using Genetic Algorithms
    (Université M'hamed Bougara de Boumerdès, 2021) Recioui, Abdelmadjid; Merdj, Mounir; Anouar, Kamli
    Fault Passage Indicators (FPIs); also named Faulted Circuit Indicators (FCIs), have been under development for the last 70 years including new capabilities to satisfy the needs of the distribution network operators. In order to improve system stability, these devices can be deployed along the feeder to reduce, or even eliminate, the uncertainty about the fault location. The number and location of FPIs affects the network reliability that can lead to extra charge on the distribution companies as well as the consumers. In this work, the optimal number and location of fault passage indicators in Power Distribution Networks (PDN) are determined. The problem is cast as an optimization task with a special economical combined objective function and solved using the genetic algorithms. The work has been tested on the two case studies, IEEE 9 bus and IEEE 33 bus systems.
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    Modeling wax disappearance temperature using advanced intelligent frameworks
    (American Chemical Society, 2019) Benamara, Chahrazed; Nait Amar, Menad; Gharbi, Kheira; Hamada, Boudjema
    The deposition of wax is one of the most potential problems that disturbs the flow assurance during production processes of hydrocarbon fluids. In this study, wax disappearance temperature (WDT) that is recognized as a vital parameter in such circumstances is modeled using advanced machine learning techniques, namely, radial basis function neural network (RBFNN) coupled with genetic algorithm (GA) and artificial bee colony (ABC). Besides, an accurate and user-friendly correlation was established by implementing the group method of data handling. Results revealed the high reliability of the proposed hybrid models and the established correlation. Moreover, RBFNN coupled with ABC (RBFNN-ABC) was found to be the best paradigm with an overall average absolute relative error value of 0.5402% and a total coefficient of determination (R2) of 0.9706. Furthermore, the performance comparison showed that RBFNN-ABC and the established explicit correlation outperform the prior intelligent and thermodynamic models. Finally, by performing the outlier detection, the quality of the utilized database was assessed, the applicability realm of the best model was delineated, and only one point was found as doubtful
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    Performance analysis and optimization of molten a salt cavity receiver in solar power plants
    (2021) Rouibah, A.; Benazzouz, D.; Rahmani, K.; Benmessaoud, T.
    The objective of this paper focuses on optimizing the performance of solar systems. These systems play an essential role in the production of electricity worldwide. They are sources of clean energy which can be exploited in areas rich with solar potential.In this paper, the goal is to optimize the heat flow absorbed by the receiver. To do this, genetic algorithms are proposed as an approach able of solving the problematic subject with constraints that are countable and interlinked. These constraints are inspired from the energy balance of the solar tower concentrator under study.The results obtained by numerical analysis based on these constraints and the objective functions (maximizing the heat flow received and minimizing the losses of the heat flow) shows the existence of an optimal receiver efficiency value for the heliostat surface total, the receiver temperature, the molten salt temperature, the receiver opening surface, the receiver surface, the diameter, the thickness, the tubes thermal conductivity of the receiver and the steam flow at turbine inlet. In addition, the energy efficiency of the solar tower system improves better depending on the power cycle chosen such as the Hirn cycle with reheating and racking used in our case
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    Efficient electronic beam steering method in time modulated linear arrays
    (IET, 2020) Gassab, Oussama; Dahimene, Abdelhakim; Bouguerra, Sara
    An efficient electronic beam steering technique in time modulated linear array (TMLA) is proposed, where the first positive and negative sidebands are utilised to implement the electronic steering process. In this technique, new periodic time sequences are used, in which a positive-ON, negative-ON, and OFF durations are utilised to obtain high sufficient steering in TMLA. Furthermore, it is shown that by using these time sequences, the non-steerable array pattern at the fundamental frequency and also the even sidebands can be eliminated and nulled to zero. In addition, the radiation power and the directivity of this proposed steered-TMLA are formulated in their closed form. The genetic algorithm is implemented to optimise the steered-TMLA by suppressing the remaining odd sidebands and increasing the power radiation at the first positive and negative sidebands. © The Institution of Engineering and Technology 2020
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    Optimization of the compression systems using genetic algorithm
    (IEEE, 2016) Zammoum Boushaki, Razika; Kessal, Farida; Bentarzi, Hamid
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    Genetic algorithm based objective functions comparative study for damage detection and localization in beam structures
    (Institute of Physics Publishing, 2015) Khatir, Samir; Belaidi, Idir; Serra, R.; Benaissa, Brahim; Ait Saada, Aicha
    The detection techniques based on non-destructive testing (NDT) defects are preferable because of their low cost and operational aspects related to the use of the analyzed structure. In this study, we used the genetic algorithm (GA) for detecting and locating damage. The finite element was used for diagnostic beams. Different structures considered may incur damage to be modelled by a loss of rigidity supposed to represent a defect in the structure element. Identification of damage is formulated as an optimization problem using three objective functions (change of natural frequencies, Modal Assurance Criterion MAC and MAC natural frequency). The results show that the best objective function is based on the natural frequency and MAC while the method of the genetic algorithm present its efficiencies in indicating and quantifying multiple damage with great accuracy. Three defects have been created to enhance damage depending on the elements 2, 5 and 8 with a percentage allocation of 50% in the beam structure which has been discretized into 10 elements. Finally the defect with noise was introduced to test the stability of the method against uncertainty
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    Bi-objective optimization algorithms for joint production and maintenance scheduling : application to the parallel machine problem
    (Springer, 2009) Berrichi, A.; Amodeo, L.; Yalaoui, F.; Châtelet, E.; Mezghiche, Mohamed
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    Use of genetic algorithms in linear and planar antenna array synthesis based on Schelkunoff method
    (2007) Recioui, Abdelmadjid; Azrar, A.
    Genetic algorithms coupled with the Schelkunoff synthesis method are used to synthesize equispaced linear and planar arrays. The purpose is to find the different excitation amplitudes and phases to achieve good matching between the desired and calculated radiation patterns. Examples which demonstrate the versatility of the approach presented in this article are considered for various patterns including the steered pattern case. The planar array is treated as a set of two separate linear arrays upon which Schelkunoff method is applied separately