Application of genetic algorithms to lubrication pump stacking design

Kelner, Vincent; Léonard, Olivier

doi:10.1016/j.cam.2003.05.022

Cited by 18 publications

(7 citation statements)

References 4 publications

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“…Genetic algorithms are based on an abstraction of the natural evolutionary behavior which was originally proposed in [20]. They are a robust and flexible approach that can be applied to a wide range of optimization problems (see, for example, [21][22][23]). Main aim of GA is to achieve better results by removing bad results during production of population from current generation to next generation and using only good results to achieve the better results.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimal variable shape parameters using genetic algorithm for radial basis function approximation

Afiatdoust

Esmaeilbeigi

2015

Ain Shams Engineering Journal

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Many radial basis function (RBF) methods contain free shape parameter or parameters that play an important role for the accuracy of the method. In most papers the authors end up choosing free shape parameter by trial and error or some other ad-hoc means. However, using variable shape parameters provides a clear potential for improved accuracy and stability of the RBF method. Already some progress has been reported to select usable variable shape parameters. In this paper, we propose applying the genetic algorithm to determine good variable shape parameters of radial basis functions for the solution of ordinary differential equations. Numerical results show that the proposed algorithm based on the genetic optimization is effective and provides reasonable shape parameters along with acceptable accuracy in linear and nonlinear case compared with other strategies to determine variable shape parameters. Ó 2014 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Section: Genetic Algorithmmentioning

confidence: 99%

Optimal variable shape parameters using genetic algorithm for radial basis function approximation

Afiatdoust

Esmaeilbeigi

2015

Ain Shams Engineering Journal

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“…Stochastic methods mainly follow three alternative approaches; genetic algorithms [49], and evolutionary strategies [50] mimic the evolutionary behavior of nature, while simulated annealing imitates the behavior of cooling fluids [51]. Stochastic algorithms have been already applied to pump stacking optimization in [52], and specifically to gerotor profiles in [53] and [54]. Several declinations of the evolutionary strategy for single-objective optimization are available in the literature [48], but they all share the same philosophy, based on the four base principles of the process of organic evolution: reproduction, mutation, competition, and selection [55].…”

Section: Stochastic Optimization Algorithmsmentioning

confidence: 99%

“…where d k+1 unmut1 and d k+1 unmut2 are the offspring's parameter vectors, d k p1 and d k p2 are the parents, and α is the crossover operator. The offspring are hence mutated by applying a random factor chosen from a standard distribution v centered in 0 with standard deviation σ(α), which is divided or multiplied by a factor α at the beginning of each iteration [52]:…”

Section: Stochastic Optimization Algorithmsmentioning

confidence: 99%

Optimization of Gerotor Pumps with Asymmetric Profiles through an Evolutionary Strategy Algorithm

2019

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Gerotor pumps are widely used for fuel and lubricating oil distribution, since they provide an economic and compact solution for low-pressure fluid systems. Made of two internally coupled gears, their behavioral and operative performances are strictly tied to their geometrical designs. Traditionally, the external gear features circular lobes that give origin to a cycloidal profile for the internal rotor. In this paper, the use of profiles based on asymmetric lobes made of elliptic arcs is further explored and expanded. At first, a complete mathematical framework describing the pump geometry and its dynamic behavior is provided, while algorithms used to compute a selected number of performance indexes are presented and when possible, verified. Hence, a single-objective optimization procedure is applied to the traditional cycloidal profile, in order to minimize each of the following quantities: the flow rate irregularity, the expected wear rate, and the estimated rotor mass. Finally, a multi-objective optimization process based on evolutionary strategy is employed, to obtain several asymmetric profiles minimizing the combination of two or more performance indexes. The results are hence compared, and the merits associated with the use of asymmetric lobes are presented.

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“…This algorithm makes the solution improved in the process of competition in order to obtain the satisfactory solution or the optimization solution [6]. It has widely used in many kinds of fields, such as function optimization [7,8], computer aid design [9], intelligent instrument design [10], scheduling optimization [11,12], steady-state operation optimization [13], robotic assembly line balancing problem [14], process planning [15], the two-dimensional assortment problem [16], intrusion detection [17], lubrication pump stacking design [18], electronic commerce [19], and so on. In this article, we use GA to solve the project scheduling problem with resource constraints so as to find out the optimal scheduling scheme.…”

Section: Introductionmentioning

confidence: 99%

Research on Project Scheduling Problem with Resource Constraints

Chen¹,

Zhou²

2013

JSW

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Resource-constrained project scheduling problem is the traditional optimization problem considering constraint conditions of resource supply, which makes the problem more difficult to solve in real life. Resource-constrained project scheduling problem is a typical NP-hard problem, which is quite popular issue in recent years. This article firstly analyzes the literature review of resource-constrained project scheduling problem. And then, the mathematical model of this problem is established, and the solution via the genetic algorithm is also developed. Finally, the numerical experiment is adopted to verify the effectiveness and efficiency of the proposed model and algorithm.

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Application of genetic algorithms to lubrication pump stacking design

Cited by 18 publications

References 4 publications

Optimal variable shape parameters using genetic algorithm for radial basis function approximation

Optimal variable shape parameters using genetic algorithm for radial basis function approximation

Optimization of Gerotor Pumps with Asymmetric Profiles through an Evolutionary Strategy Algorithm

Research on Project Scheduling Problem with Resource Constraints

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