Article title: Using response surface design to determine the optimal parameters of genetic algorithm and a case study
Usage guidelinesThis version is made available online in accordance with publisher policies. To see the final version of this paper, please visit the publisher's website (a subscription may be required to access the full text).Before reusing this item please check the rights under which it has been made available. Some items are restricted to non-commercial use. Please cite the published version where applicable. Using response surface design to determine the optimal parameters of genetic algorithm and a case study Ibrahim Abstract: Genetic algorithms are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP hard problems. This algorithm includes a number of parameters whose different levels affect the performance of the algorithm strictly. The general approach to determine the appropriate parameter combination of genetic algorithm depends on too many trials of different combinations and the best one of the combinations that produces good results is selected for the program that would be used for problem solving. A few researchers studied on parameter optimisation of genetic algorithm. In this paper, response surface depended parameter optimisation is proposed to determine the optimal parameters of genetic algorithm. Results are tested for benchmark problems that is most common in mixed-model assembly line balancing problems of type-I (MMALBP-I). Using response surface design to determine the optimal parameters of genetic algorithm and a case studyGenetic algorithms are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP hard problems. This algorithm includes a number of parameters whose different levels affect the performance of the algorithm strictly. The general approach to determine the appropriate parameter combination of genetic algorithm depends on too many trials of different combinations and the best one of the combinations that produces good results is selected for the program that would be used for problem solving. A few researchers studied on parameter optimisation of genetic algorithm. In this paper, response surface depended parameter optimisation is proposed to determine the optimal parameters of genetic algorithm. Results are tested for benchmark problems that is most common in mixed-model assembly line balancing problems of type-I (MMALBP-I).
The objective of this study was to investigate the effect of fuel injection timing and engine speed on engine performance and exhaust emission parameters using a diesel engine running on canola oil methyl ester (COME). COME was produced by means of the transesterification method and tested at full load with various engine speeds by changing fuel injection timing (12, 15, and 18 CA) in a turbocharged direct injection (TDI) diesel engine. The experiments were designed using response surface methodology (RSM), which is one of the well-known design of experiment technique for predicting the responses engine performance and exhaust emission parameters from a second order polynomial equation obtained by modeling the relation between fuel injection timing (t) and engine speed (n) parameters. By using the second order full quadratic RSM models obtained from experimental results, responses brake power, brake torque, brake mean effective pressure, brake specific fuel consumption, brake thermal efficiency, exhaust gas temperature, oxygen (O 2 ), oxides of nitrogen (NO x ), carbon dioxide (CO 2 ), carbon monoxide (CO), and light absorption coefficient (K) affected from factors t and n were able to be predicted by 95% confidence interval. V C 2013 AIP Publishing LLC.
An experimental investigation was conducted to evaluate the suitability of hazelnut oil methyl ester (HOME) for engine performance and exhaust emissions responses of a turbocharged direct injection (TDI) diesel engine. HOME was tested at full load with various engine speeds by changing fuel injection timing (12, 15, and 18 deg CA) in a TDI diesel engine. Response surface methodology (RSM) and least-squares support vector machine (LSSVM) were used for modeling the relations between the engine performance and exhaust emission parameters, which are the measured responses and factors such as fuel injection timing (t) and engine speed (n) parameters as the controllable input variables. For this purpose, RSM and LSSVM models from experimental results were constructed for each response, namely, brake power, brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), exhaust gas temperature (EGT), oxides of nitrogen (NOx), carbon dioxide (CO2), carbon monoxide (CO), and smoke opacity (N), which are affected by the factors t and n. The results of RSM and LSSVM were compared with the observed experimental results. These results showed that RSM and LSSVM were effective modeling methods with high accuracy for these types of cases. Also, the prediction performance of LSSVM was slightly better than that of RSM.
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