Parameter’s setting of the ant colony algorithm applied in preventive maintenance optimization

Samrout, Mohamad; Kouta, Raed; Yalaoui, Farouk; Châtelet, Éric; Chebbo, Nazir

doi:10.1007/s10845-007-0039-3

Cited by 17 publications

(7 citation statements)

References 30 publications

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“…We can simply locate the minimal error interval and choose the corresponding values. Thus, table 8 shows the ACO algorithm parameters' values for the three cases of study, based on the analysis above and previous work in literature [35] [37]. Actually, there is a slight error decrease regarding the values used in Section 5.3, Section 5.4 and Section 5.5 (first trial) and a slight modification of the optimal values of operators' parameters (Table.…”

Section: Evaporation Coefficientmentioning

confidence: 95%

“…We rely on literature to fix these parameters in this part. The ACO algorithm is applied using 30 ants, and based on research studies of ACO parameter selection on particular problems [34] [35], pheromone intensity Q is fixed to 100, evaporation coefficient to 0,5, to 1, to 5 and to 1. We consider that the desirability to choose the next node (i.e.…”

Section: Optimal Parametersmentioning

confidence: 99%

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An ant colony based model to optimize parameters in industrial vision

Benchikhi

Sadgal²,

Elfazziki³

et al. 2017

ELCVIA

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Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.

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Section: Evaporation Coefficientmentioning

confidence: 95%

Section: Optimal Parametersmentioning

confidence: 99%

An ant colony based model to optimize parameters in industrial vision

Benchikhi

Sadgal²,

Elfazziki³

et al. 2017

ELCVIA

View full text Add to dashboard Cite

show abstract

“…The larger Q is, the faster the ACO algorithm converges, while the ACO algorithm is easy to fall into the local optimal region [17]. With regard to these parameters of ACO, Dorigo et al [19], Botee and Bonabeau [20], Duan et al [18], and Samrout et al [21] studied parameters selection of ACO for particular problems. Based on these research results, evaporation coefficient ρ is set as 0.9, pheromone intensity Q as 100, and the initial value of the pheromone quantity τ 0 ij as 100 in this study.…”

Section: Grid-based Aco Algorithm For Parameters Optimization Of Svmmentioning

confidence: 99%

Fault diagnosis based on support vector machines with parameter optimization by an ant colony algorithm

Zhang

Chen

2009

Proceedings of the Institution of Mechanical Engineers, Part C:

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Since support vector machines (SVM) exhibit a good generalization performance in the small sample cases, these have a wide application in machinery fault diagnosis. However, a problem arises from setting optimal parameters for SVM so as to obtain optimal diagnosis result. This article presents a fault diagnosis method based on SVM with parameter optimization by ant colony algorithm to attain a desirable fault diagnosis result, which is performed on the locomotive roller bearings to validate its feasibility and efficiency. The experiment finds that the proposed algorithm of ant colony optimization with SVM (ACO—SVM) can help one to obtain a good fault diagnosis result, which confirms the advantage of the proposed ACO—SVM approach.

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“…In such situations, obtaining the exact global optimum using analytical approaches via mathematical inference is impractical. Therefore, the metaheuristic algorithms, such as a genetic algorithm (GA) (Bris et al 2003;Marseguerra and Zio 2000;Jia et al 2003;Zimin Yang et al 2008), colony algorithm (Samrout et al 2005(Samrout et al , 2007 and simulated annealing (Leou 2006), are widely employed to optimize these models and approach the global optimum. Of these heuristic algorithms, GAs are also widely used to efficiently solving problems associated with reliability structural design and redundancy allocations (Gen and Cheng 1997;Hsieh et al 1998;Tavakkoli-Moghaddam et al 2008).…”

Section: Introductionmentioning

confidence: 99%

A hybrid genetic algorithm to minimize the periodic preventive maintenance cost in a series-parallel system

Lin

Wang

2010

J Intell Manuf

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This study presents a hybrid genetic algorithm (GA) to optimize the periodic preventive maintenance model in a series-parallel system. The intrinsic properties of a repairable system, including the structure of reliability block diagrams, maintenance priority of components, and their maintenance periods, are considered in developing the proposed hybrid GA. The importance measure of components is employed to account for these properties, identify important components, and determine their maintenance priorities. The optimal maintenance periods of these important components are then determined to minimize total maintenance cost given the allowable worst reliability of a repairable system using the GA search mechanism. An elitist conservation strategy is applied to retain superior chromosomes in the iterative breeding process to accelerate the approach toward the global optimum. Furthermore, the response surface methodology is utilized to systematically determine crossover probability and mutation probability in the GA instead of using the conventional trial-and-error process. A case study demonstrates the effectiveness and practicality of the proposed hybrid GA in optimizing the periodic preventive maintenance model in a series-parallel system.

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Parameter’s setting of the ant colony algorithm applied in preventive maintenance optimization

Cited by 17 publications

References 30 publications

An ant colony based model to optimize parameters in industrial vision

An ant colony based model to optimize parameters in industrial vision

Fault diagnosis based on support vector machines with parameter optimization by an ant colony algorithm

A hybrid genetic algorithm to minimize the periodic preventive maintenance cost in a series-parallel system

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