Construction site layout planning using multi-objective artificial bee colony algorithm with Levy flights

Yahya, M. H. M.; Saka, M.P.

doi:10.1016/j.autcon.2013.11.001

Cited by 137 publications

(61 citation statements)

References 18 publications

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“…In this approach, the position of objects is identified by a unique location reference that is assigned to grid cells (Fig. 1b) (Cheung et al 2002;Elbeltagi 1999, 2000;Elbeltagi et al , 2004Osman et al 2003;Khalafallah and El-Rayes 2006a, 2006b, 2008Ning et al 2010;Yahya and Saka 2014). This approach facilitates the search process and the identification of space conflicts between objects during the search.…”

Section: Grid Systemmentioning

confidence: 99%

The constructs of site layout modeling: an overview

Sadeghpour

Andayesh

2015

Can. J. Civ. Eng.

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Abstract:The efficient planning of site space through the course of a construction project is referred to as site layout planning. Due to its impact on safety, productivity and security on construction sites, several site layout planning models have been developed in the past decades. These models have the common aim of generating best layouts considering the defined constraints and conditions. However, the underlying assumptions that were made during the development of these models seem disparate and often implicit. This study provides an overview of the existing models and aims to draw a holistic view of variables that have been considered at different levels of detail and using different approaches in the site layout literature. Through close examination and comparative analysis of existing models, this study identifies the components that need to be considered for site layout modeling, referred to as constructs. Possible approaches that can be used to realize each construct are presented, and the advantages and disadvantages of these approaches are discussed. It is hoped that this study contributes to a better understanding of site layout modeling, and provide an outline for the development of new site layout planning models.Key words: construction site planning, site layout modeling, modeling constructs, site management, optimization models.Résumé : La planification de l'agencement d'un espace défini dans le cadre d'un chantier de construction est appelée « plan d'aménagement de site ». En raison de l'impact de ce dernier sur la sécurité, la productivité et la sûreté des sites de construction, de nombreux modèles de plans d'aménagement de site ont été développés au cours des dernières décennies. Ces modèles ont pour objectif commun de concevoir des aménagements optimaux en tenant compte de contraintes et de conditions précises. Cependant, les hypothèses de base émises lors de l'élaboration de ces modèles semblent nombreuses et souvent implicites. La présente étude fournit un aperçu des modèles existants et a pour but d'offrir une vision globale des variables qui ont été étudiées à différents niveaux de détail et sous diverses approches dans la littérature traitant de l'aménagement de site. Grâce à un examen attentif et à une analyse comparative des modèles existants, le présent article identifie les composants, nommés « structures », à prendre en compte dans la modélisation de l'aménagement de site. L'article décrit également les différentes approches pouvant être adoptées pour créer chaque structure ainsi que les avantages et désavantages de ces approches. Il est à espérer que la présente étude contribuera à une meilleure compréhension de la modélisation de l'aménagement de site et constituera un point de départ à l'élaboration de nouveaux modèles de plans d'aménagement de site. [Traduit par le Rédaction] Mots-clés : plan de site de construction, modélisation d'aménagement de site, structures de modélisation, gestion de site, modèles d'optimisation.

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Section: Grid Systemmentioning

confidence: 99%

The constructs of site layout modeling: an overview

Sadeghpour

Andayesh

2015

Can. J. Civ. Eng.

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“…The exploratory short jumps can ensure they have a finely local search in their landscape, and the occasionally long jumps can help them get into another area to make more extensive search. Considering the merits of Levy flight, such strategy has been applied to optimization and optimal search, and preliminary results have shown its promising capability [12] [13].…”

Section: Design Of Ifoamentioning

confidence: 99%

“…Updating the best fruit fly and global best fruit fly according to the fitness value. Then divided the group into two subgroups according to (10) and update the location of the fruit flies in each subgroups according to (11) and (12).…”

Section: Parameters Optimization Of Svm With Ifoamentioning

confidence: 99%

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Parameters Optimization of SVM Based on Improved FOA and Its Application in Fault Diagnosis

Zhang¹,

Fang²

2015

JSW

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Abstract:In most cases, fault diagnosis is essentially a pattern recognition problem and support vector machine (SVM) provides a new solution for the diagnosis problem of systems in which the fault samples are few. However, the parameters selection in SVM has significant influence on the diagnosis performance. In this paper, improved fruit fly optimization algorithm (IFOA), which is basically the standard fruit fly optimization algorithm (FOA) combined with Levy flight search strategy, is proposed to determine the SVM parameters. Some benchmark datasets are used to evaluate the proposed algorithm. Furthermore, the proposed method is used to diagnose the faults of hydraulic pump. Experiments and engineering application show that the proposed method outperforms standard FOA, genetic algorithm (GA) and particle swarm optimization (PSO) methods.

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Section: B the Proposed Algorithm Ifoamentioning

confidence: 99%

Research on Parameters Optimization of SVM Based on Improved Fruit Fly Optimization Algorithm

Zhang¹,

Fang²,

Ma³

et al. 2016

IJCTE

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Abstract-The performance of the support vector machine (SVM) is determined to a great extent by the parameter selection. In order to improve the learning and generalization ability of SVM, in this paper, an improved fruit fly optimization algorithm (IFOA) was proposed to optimize kernel parameter and penalty factor of SVM. In IFOA, the fruit fly group is dynamically divided into advanced subgroup and drawback subgroup according to its own evolutionary level. A global search is made for the drawback subgroup under the guidance of the best individual and a finely local search is made for the advanced subgroup in which the fruit flies do Levy flight around the best individual. Two subgroups exchange information by updating the overall optimum and recombining the subgroups. Getting rid of local optimum and improve search ability are ensured by making those changes in basic FOA. The performance of the IFOA and classification accuracy of optimized SVM based on IFOA are respectively examined through several typical benchmark functions and classical data sets from UCI benchmark. The experiment results show that the performance of the new algorithm is obviously more successful than FOA and it is also an effective SVM parameter optimization method which has better performance than some other methods.Index Terms-Support vector machine, fruit fly optimization algorithm, parameter optimization, Levy flight.

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Construction site layout planning using multi-objective artificial bee colony algorithm with Levy flights

Cited by 137 publications

References 18 publications

The constructs of site layout modeling: an overview

The constructs of site layout modeling: an overview

Parameters Optimization of SVM Based on Improved FOA and Its Application in Fault Diagnosis

Research on Parameters Optimization of SVM Based on Improved Fruit Fly Optimization Algorithm

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