Hybrid multi-objective shape design optimization using Taguchi’s method and genetic algorithm

Yıldız, Ali Rıza; Öztürk, Nursel; Kaya, Necmettin; Öztürk, Ferruh

doi:10.1007/s00158-006-0079-x

Cited by 64 publications

(8 citation statements)

References 38 publications

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“…A research based on a new hybrid approach, which deals with the improvement of shape optimization process, is given in [53]. An integrated and optimized product design framework to support the design optimization applications in concurrent engineering is given to show the effectiveness of hybrid approaches and how they can be used to improve the performance of integrated design optimization applications in [54].…”

Section: Related Workmentioning

confidence: 99%

Robust hyper-heuristic algorithms for the offline oriented/non-oriented 2D bin packing problems

Beyaz

Dökeroğlu

Çoşar

2015

Applied Soft Computing

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Section: Related Workmentioning

confidence: 99%

Robust hyper-heuristic algorithms for the offline oriented/non-oriented 2D bin packing problems

Beyaz

Dökeroğlu

Çoşar

2015

Applied Soft Computing

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“…Ray and Liew [22] used a swarm metaphor approach in which a new optimization algorithm based on behavioural concepts similar to real swarm was proposed to solve the same problem. Yıldız et al [23] used hybrid robust genetic algorithm combining Taguchi's method and genetic algorithm. The combination of genetic algorithm with robust parameter design through a smaller population of individuals resulted in a solution that lead to better parameter values for design optimization problems.…”

Section: Previous Work On Disc Brakementioning

confidence: 99%

Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

Sabarinath¹,

Thansekhar²,

Saravanan³

2015

Journal of Applied Mathematics

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The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.

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“…To avoid confusion with common wireless communications terminology, this article will refer to this as SNR Tag . There are three distinct formulas for calculation of SNR Tag : (1) lower-is-better (LIB) as shown in 1; (2) higher-is-better (HIB) as shown in 2; and (3) nominal-is-better (NIB) as shown in 3 [20]. Each sample of the metric under consideration is denoted as y i and y is the overall sample mean given from n replicates.…”

Section: Taguchi Signal-to-noise Ratiomentioning

confidence: 99%

Cognitive radio engine parametric optimization utilizing Taguchi analysis

Amanna

Ali

Gadhiok

et al. 2012

J Wireless Com Network

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Cognitive radio (CR) engines often contain multiple system parameters that require careful tuning to obtain favorable overall performance. This aspect is a crucial element in the design cycle yet is often addressed with ad hoc methods. Efficient methodologies are required in order to make the best use of limited manpower, resources, and time. Statistical methods for approaching parameter tuning exist that provide formalized processes to avoid inefficient ad hoc methods. These methods also apply toward overall system performance testing. This article explores the use of the Taguchi method and orthogonal testing arrays as a tool for identifying favorable genetic algorithm (GA) parameter settings utilized within a hybrid case base reasoning/genetic algorithm CR engine realized in simulation. This method utilizes a small number of test cases compared to traditional design of experiments that rely on full factorial combinations of system parameters. Background on the Taguchi method, its drawbacks and limitations, past efforts in GA parameter tuning, and the use of GA within CR are overviewed. Multiple CR metrics are aggregated into a single figure-of-merit for quantification of performance. Desirability functions are utilized as a tool for identifying ideal settings from multiple responses. Kiviat graphs visualize overall CR performance. The Taguchi method analysis yields a predicted best combination of GA parameters from nine test cases. A confirmation experiment utilizing the predicted best settings is compared against the predicted mean, and desirability. Results show that the predicted performance falls within 1.5% of the confirmation experiment based on 9 test cases as opposed to the 81 test cases required for a full factorial design of experiments analysis.

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Hybrid multi-objective shape design optimization using Taguchi’s method and genetic algorithm

Cited by 64 publications

References 38 publications

Robust hyper-heuristic algorithms for the offline oriented/non-oriented 2D bin packing problems

Robust hyper-heuristic algorithms for the offline oriented/non-oriented 2D bin packing problems

Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

Cognitive radio engine parametric optimization utilizing Taguchi analysis

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