Robust Design using Pareto type optimization: A genetic algorithm with arithmetic crossover

Köksoy, Onur; Yalçınöz, Tankut

doi:10.1016/j.cie.2007.11.019

Cited by 43 publications

(13 citation statements)

References 13 publications

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“…Thus, parameter a r may be calculated using expression (10): (10) From expressions (3) through (8), one can see that in the fitness calculation, the GA needs to know the responses corresponding to the experimental treatment, which each chromosome represents. However, some of those treatments might not have been part of the experiment that the engineer conducted to gather the data.…”

Section: Some Details Of the Genetic Algorithm Used In Robust Designmentioning

confidence: 99%

“…Given that other researchers have also explored the application of GA to robust design (e.g. [2,9,10] to name just a few), it was sensible to apply the same optimization algorithm for developing such tool. Some newer optimization algorithms might also have been applied, but there is no evidence that they could always and consistently outperform GAs [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Performance of a Genetic Algorithm applied to robust design in multiobjective systems under different levels of fractioning

Canessa

Allende

2015

Rev. Fac. Ing. Antioquia

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This paper studies the performance of a Genetic Algorithm (GA) to find solutions to problems of robust design in multiobjective systems with many control and noise factors, representing the output vector in a single aggregation function. The results show that the GA is able to find solutions that achieve a good adjustment of the responses to their corresponding target values and with low variability, even with highly fractional experimental designs, which provide a limited number of data points to be fed to the GA. This conclusion is important for the practical application of the GA to robust design studies. Generally, such studies are carried out using scarce resources and dealing with other limitations, which force the engineer to use few experimental treatments and gather a limited amount of data. Thus, knowing that the GA performs well under such situation expands its applicability. ----------Keywords: Taguchi methods, parameter design, genetic algorithms, performance analysis ResumenEste trabajo estudia el rendimiento de un Algoritmo Genético (AG) para encontrar soluciones a problemas de diseño robusto en sistemas multiobjetivo, con muchos factores de control y ruido, representando el vector de salida * Corresponding author: Enrique Canessa Terrazas, e-mail: ecanessa@uai.cl DOI: 10.17533/udea.redin.n75a09 81Performance of a Genetic Algorithm applied to robust design in multiobjective systems... en una sola función de agregación. Los resultados muestran que el AG es capaz de encontrar soluciones que entregan un buen ajuste de la media de las respuestas a sus respectivos valores objetivo y con baja variabilidad, incluso con diseños experimentales altamente fraccionados, los cuales proveen de un número limitado de datos que se ingresan al AG. Esta conclusión es importante para la aplicación práctica del AG a estudios de diseño robusto. Generalmente, dichos estudios son llevados a cabo usando recursos escasos y lidiando con otras limitaciones, lo que obliga al ingeniero a usar pocos tratamientos y recoger una cantidad limitada de datos. Por eso, saber que el AG se comporta bien bajo esas situaciones, expande su aplicabilidad.----------Palabras clave: métodos de Taguchi, diseño de parámetros, algoritmos genéticos, análisis de rendimiento

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Section: Some Details Of the Genetic Algorithm Used In Robust Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance of a Genetic Algorithm applied to robust design in multiobjective systems under different levels of fractioning

Canessa

Allende

2015

Rev. Fac. Ing. Antioquia

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“…In addition, another randomly-selected waypoint from available waypoints in the airspace was inserted at a random position in the route under consideration. The mutation process was modified to improve global exploration and ensure that there was diversity in generated solutions [17]. The rate at which mutation was done was determined by the mutation probability parameter.…”

Section: Mutation Processmentioning

confidence: 99%

An Improved Genetic Algorithm for Flight Path Re-Routes with Reduced Passenger Impact

Ayo¹

2017

JCC

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Adverse weather has serious implications for flight timeliness, as well as passenger and aircraft safety. This often implies that alternative flight paths have to be used by aircraft to avoid adverse weather. To reduce the impact of such path re-routes, exact techniques such as artificial potential field model and Dijkstra's algorithms have been proposed. However, such approaches are often unsuitable for real time scenarios involving large number of waypoints and constraints. This has led to the use of metaheuristic techniques that give sub-optimal solutions in good time. In this work, an improved genetic algorithm-based technique has been proposed. The algorithm used an improved mutation operator, reduced passenger inconvenience and considered the schedules of aircraft.

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“…Figure 5 depicts parent chromosomes X and Y , each composed of n components (genes). O spring chromosomes are generated via the following relationships [38]: In this research, in order to diversify reproduced solutions, the value of is randomly generated in the range of [ ; 1+ ]( 0). Selecting a higher value for allows for producing higher or lower values than the values for the parent chromosomes, so as to enhance diversi cation across the reproduced solutions.…”

Section: Crossover Operatormentioning

confidence: 99%

A bi-level programming model for decentralized manufacturer-distributer supply chain considering cooperative advertising

2017

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Abstract. This paper studies a bi-level decentralized supply chain consisting of one manufacturer and one distributor. Along with global advertising, the manufacturer participates in part of local advertising expenditure incurred by the distributor. Bi-level programming is applied to model the relationship between the manufacturer and distributer under two power scenarios of Stackelberg game framework. In the rst scenario, we consider the manufacturer as the leader and in the latter, we allow the distributer to act as the dominant member of the supply chain. In order to tackle bi-level programming models, a meta-heuristic genetic algorithm with hierarchical structure is developed for each scenario and optimal policies for the members in terms of pricing, advertising, inventory, and demand allocation are determined. Finally, several computational experiments are conducted on the data obtained from an automotive spare parts supply chain to demonstrate the validity of the developed models and compare the bene ts of members as well as of the entire system.

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Robust Design using Pareto type optimization: A genetic algorithm with arithmetic crossover

Cited by 43 publications

References 13 publications

Performance of a Genetic Algorithm applied to robust design in multiobjective systems under different levels of fractioning

Performance of a Genetic Algorithm applied to robust design in multiobjective systems under different levels of fractioning

An Improved Genetic Algorithm for Flight Path Re-Routes with Reduced Passenger Impact

A bi-level programming model for decentralized manufacturer-distributer supply chain considering cooperative advertising

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