Studying the SPEA2 algorithm for optimising a pattern-recognition based machine translation system

Sofianopoulos, Sokratis; Tambouratzis, George

doi:10.1109/smdcm.2011.5949279

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…If the initial training data contains Iter_max samples, this procedure repeats Iter_max times (it equals the number of observations in the initial training set). Then, the average of these errors is reported as the predictive error of our predictive model across the entire data set in terms of expressions by equation (2). Thus, with this validation method, in its iterations Iter_max, all patterns have the opportunity to act as a prototype.…”

Section: Many-objective Optimization Spea/r Algorithmmentioning

confidence: 99%

“…1 who have proposed a multi-objective optimization for RF circuit blocks through replacement models and NBI and SPEA2 methods. Sofianopoulos and Tambouratzis 2 proposed a model-based machine translation system using large monolithic blocks in the target language from which statistical information was extracted. This study reported using a specific machine translation to represent the test that SPEA2 was chosen as the optimization method.…”

Section: Introductionmentioning

confidence: 99%

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New hybrid between SPEA/R with deep neural network: Application to predicting the multi-objective optimization of the stiffness parameter for powertrain mount systems

Dao

2019

Journal of Low Frequency Noise, Vibration and Active Control

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In this study, a new methodology, hybrid Strength Pareto Evolutionary Algorithm Reference Direction (SPEA/R) with Deep Neural Network (HDNN&SPEA/R), has been developed to achieve cost optimization of stiffness parameter for powertrain mount systems. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration of a rear engine mount, mean square displacement of a rear engine mount, mean square acceleration of a front left engine mount, mean square displacement of a front left engine mount, mean square acceleration of a front right engine mount, and mean square displacement of a front right engine mount. A hybrid HDNN&SPEA/R is proposed with the integration of genetic algorithm, deep neural network, and a Strength Pareto evolutionary algorithm based on reference direction for multi-objective SPEA/R. Several benchmark functions are tested, and results reveal that the HDNN&SPEA/R is more efficient than the typical deep neural network. stiffness parameter for powertrain mount systems optimization with HDNN&SPEA/R is simulated, respectively. It proved the potential of the HDNN&SPEA/R for stiffness parameter for powertrain mount systems optimization problem.

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Section: Many-objective Optimization Spea/r Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New hybrid between SPEA/R with deep neural network: Application to predicting the multi-objective optimization of the stiffness parameter for powertrain mount systems

Dao

2019

Journal of Low Frequency Noise, Vibration and Active Control

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“…SPEA 2 and NSGA-II have been implemented to solve a lot of problems in the Multiobjective and Combinatorial Optimization fiel. For instance, problems such as Pattern-recognition based Machine Translation System (Sofianopoulos & Tambouratzis, 2011), Tuning of Fuzzy Logic controllers for a heating (Gacto et al, 2011), Real-coded Quantum Clones (Xiawen & Yu, 2011), Optimization Problems with Correlated Objectives (Ishibuchi et al, 2011), Production Planning , Optical and Dynamic Networks Designs (Araujo et al, 2011;Wismans et al, 2011), Benchmark multi-objective …”

Section: Tabu Searchmentioning

confidence: 99%

Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems

Niño-Ruiz¹

2012

Real-World Applications of Genetic Algorithms

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“…De Tommasi et al 14 have proposed a multiobjective optimization for radio-frequency circuit blocks through replacement models and NBI and SPEA2 methods. Sokratis and George 15 proposed a model-based machine translation system using large monolithic blocks in the target language from which statistical information was extracted. This study reported using a specific machine translation to represent the test that SPEA2 was chosen as the optimization method.…”

Section: Introductionmentioning

confidence: 99%

New hybrid approach HNSGA-III&SPEA/R: Application to optimization of powertrain mount system stiffness parameters

Truong

Dao

2021

Journal of Low Frequency Noise, Vibration and Active Control

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In this study, a new methodology, hybrid NSGA-III with SPEA/R (HNSGA-III&SPEA/R), has been developed to design and achieve cost optimization of powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the powertrain mount system. A hybrid HNSGA-III&SPEA/R is proposed with the integration of Strength Pareto evolutionary algorithm based on reference direction for Multi-objective (SPEA/R) and Many-objective optimization genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&SPEA/R is more efficient than the typical SPEA/R, NSGA-III. Powertrain mount system stiffness parameters optimization with HNSGA-III&SPEA/R is simulated respectively. It proved the potential of the HNSGA-III&SPEA/R for powertrain mount system stiffness parameter optimization problem.

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Studying the SPEA2 algorithm for optimising a pattern-recognition based machine translation system

Cited by 6 publications

References 11 publications

New hybrid between SPEA/R with deep neural network: Application to predicting the multi-objective optimization of the stiffness parameter for powertrain mount systems

New hybrid between SPEA/R with deep neural network: Application to predicting the multi-objective optimization of the stiffness parameter for powertrain mount systems

Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems

New hybrid approach HNSGA-III&SPEA/R: Application to optimization of powertrain mount system stiffness parameters

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