Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

Browse

Subject: search.filters.subject.Genetic algorithms

Search Results

Now showing 1 - 8 of 8
  • No Thumbnail Available
    Item
    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
  • No Thumbnail Available
    Item
    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
  • No Thumbnail Available
    Item
    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
  • No Thumbnail Available
    Item
  • No Thumbnail Available
    Item
    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
  • No Thumbnail Available
    Item
    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
  • No Thumbnail Available
    Item
    Synthesis of linear arrays with sidelobe level reduction constraint using genetic algorithm
    (2008) Recioui, Abdelmadjid; Azrar, Arab; Bentarzi, Hamid; Dehmas, Mokrane; Chalal, Mouloud
    The synthesis of uniformly spaced linear array geometries with minimum sidelobe level and beamforming capability using genetic algorithms is presented. The iterative process aims not only at matching the desired pattern to the desired one but minimizing the sidelobe level as well; through optimizing the element excitations. Various examples are included to demonstrate the design effectiveness and flexibility namely for switched smart antenna systems applications
  • No Thumbnail Available
    Item
    Genetic algorithm based MIMO capacity enhancement In spatially correlated channels including mutual coupling
    (Springer, 2012) Recioui, Abdelmadjid; Bentarzi, Hamid
    Higher system capacities can be achieved if multiple antennas are used on both sides of the wireless link, thus creating a multiple-input-multiple-output (MIMO) system. In this work, the maximization of MIMO system capacity in Rayleigh fading, spatially correlated channels involving practical antenna arrays is challenged through inter-element spacing optimization. The system capacity is evaluated using a proposed formula that takes into account both antenna mutual coupling and signal correlation. Capacity values turn out to outperform the ones obtained considering the conventional antenna array geometries