Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling

In decision theory, the weighted sum model (WSM) is the best known Multi-Criteria Decision Analysis (MCDA) approach for evaluating a number of alternatives in terms of a number of decision criteria. Assigning weights is a difficult task, especially if the number of criteria is large and the criteria...

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Main Author:	Witwit, Azher Razzaq Hadi
Format:	Thesis
Language:	eng eng
Published:	2017
Subjects:	QA299.6-433 Analysis
Online Access:	https://etd.uum.edu.my/7008/1/s93019_01.pdf https://etd.uum.edu.my/7008/2/s93019_02.pdf
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id	my-uum-etd.7008
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institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Yasin, Azman Horizon, Gitano
topic	QA299.6-433 Analysis
spellingShingle	QA299.6-433 Analysis Witwit, Azher Razzaq Hadi Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling
description	In decision theory, the weighted sum model (WSM) is the best known Multi-Criteria Decision Analysis (MCDA) approach for evaluating a number of alternatives in terms of a number of decision criteria. Assigning weights is a difficult task, especially if the number of criteria is large and the criteria are very different in character. There are some problems in the real world which utilize conflicting criteria and mutual effect. In the field of automotive, the knocking phenomenon in internal combustion or spark ignition engines limits the efficiency of the engine. Power and fuel economy can be maximized by optimizing some factors that affect the knocking phenomenon, such as temperature, throttle position sensor, spark ignition timing, and revolution per minute. Detecting knocks and controlling the above factors or criteria may allow the engine to run at the best power and fuel economy. The best decision must arise from selecting the optimum trade-off within the above criteria. The main objective of this study was to proposed a new Non-Weighted Aggregate Evaluation Function (NWAEF) model for non-linear multi-objectives function which will simulate the engine knock behavior (non-linear dependent variable) in order to optimize non-linear decision factors (non-linear independent variables). This study has focused on the construction of a NWAEF model by using a curve fitting technique and partial derivatives. It also aims to optimize the nonlinear nature of the factors by using Genetic Algorithm (GA) as well as investigate the behavior of such function. This study assumes that a partial and mutual influence between factors is required before such factors can be optimized. The Akaike Information Criterion (AIC) is used to balance the complexity of the model and the data loss, which can help assess the range of the tested models and choose the best ones. Some statistical tools are also used in this thesis to assess and identify the most powerful explanation in the model. The first derivative is used to simplify the form of evaluation function. The NWAEF model was compared to Random Weights Genetic Algorithm (RWGA) model by using five data sets taken from different internal combustion engines. There was a relatively large variation in elapsed time to get to the best solution between the two model. Experimental results in application aspect (Internal combustion engines) show that the new model participates in decreasing the elapsed time. This research provides a form of knock control within the subspace that can enhance the efficiency and performance of the engine, improve fuel economy, and reduce regulated emissions and pollution. Combined with new concepts in the engine design, this model can be used for improving the control strategies and providing accurate information to the Engine Control Unit (ECU), which will control the knock faster and ensure the perfect condition of the engine.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	Witwit, Azher Razzaq Hadi
author_facet	Witwit, Azher Razzaq Hadi
author_sort	Witwit, Azher Razzaq Hadi
title	Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling
title_short	Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling
title_full	Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling
title_fullStr	Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling
title_full_unstemmed	Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling
title_sort	non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2017
url	https://etd.uum.edu.my/7008/1/s93019_01.pdf https://etd.uum.edu.my/7008/2/s93019_02.pdf
_version_	1747828145604526080
spelling	my-uum-etd.70082021-08-18T08:29:03Z Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling 2017 Witwit, Azher Razzaq Hadi Yasin, Azman Horizon, Gitano Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA299.6-433 Analysis In decision theory, the weighted sum model (WSM) is the best known Multi-Criteria Decision Analysis (MCDA) approach for evaluating a number of alternatives in terms of a number of decision criteria. Assigning weights is a difficult task, especially if the number of criteria is large and the criteria are very different in character. There are some problems in the real world which utilize conflicting criteria and mutual effect. In the field of automotive, the knocking phenomenon in internal combustion or spark ignition engines limits the efficiency of the engine. Power and fuel economy can be maximized by optimizing some factors that affect the knocking phenomenon, such as temperature, throttle position sensor, spark ignition timing, and revolution per minute. Detecting knocks and controlling the above factors or criteria may allow the engine to run at the best power and fuel economy. The best decision must arise from selecting the optimum trade-off within the above criteria. The main objective of this study was to proposed a new Non-Weighted Aggregate Evaluation Function (NWAEF) model for non-linear multi-objectives function which will simulate the engine knock behavior (non-linear dependent variable) in order to optimize non-linear decision factors (non-linear independent variables). This study has focused on the construction of a NWAEF model by using a curve fitting technique and partial derivatives. It also aims to optimize the nonlinear nature of the factors by using Genetic Algorithm (GA) as well as investigate the behavior of such function. This study assumes that a partial and mutual influence between factors is required before such factors can be optimized. The Akaike Information Criterion (AIC) is used to balance the complexity of the model and the data loss, which can help assess the range of the tested models and choose the best ones. Some statistical tools are also used in this thesis to assess and identify the most powerful explanation in the model. The first derivative is used to simplify the form of evaluation function. The NWAEF model was compared to Random Weights Genetic Algorithm (RWGA) model by using five data sets taken from different internal combustion engines. There was a relatively large variation in elapsed time to get to the best solution between the two model. Experimental results in application aspect (Internal combustion engines) show that the new model participates in decreasing the elapsed time. This research provides a form of knock control within the subspace that can enhance the efficiency and performance of the engine, improve fuel economy, and reduce regulated emissions and pollution. Combined with new concepts in the engine design, this model can be used for improving the control strategies and providing accurate information to the Engine Control Unit (ECU), which will control the knock faster and ensure the perfect condition of the engine. 2017 Thesis https://etd.uum.edu.my/7008/ https://etd.uum.edu.my/7008/1/s93019_01.pdf text eng public https://etd.uum.edu.my/7008/2/s93019_02.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Aarts, d. i. R. G. K. M. (2011). System Identification and Parameter Estimation (edition: 2011/2012 ed.). 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Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling

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