Improve micro genetic algorithm for multiobjective kursawe function and low pass filter circuit design optimization

Although Evolutionary Algorithms (EAs) have been widely implemented for solving Multiobjective Optimization Problems (MOPs), the convergence of EAs towards Pareto optimal front is still an issue of concern. In order to enhance the robustness of EAs, hybrid algorithms are commonly developed to ident...

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Format:	Thesis
Language:	English
Subjects:	Evolutionary computation Computer algorithms Algorithms Evolutionary Algorithms (EAs) Multiobjective Optimization Problems (MOPs)
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72831/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72831/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72831/4/Lim%20Wei%20Jer.pdf
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spelling	my-unimap-728312021-11-18T02:13:05Z Improve micro genetic algorithm for multiobjective kursawe function and low pass filter circuit design optimization Asral, Bahari Jambek, Dr. Although Evolutionary Algorithms (EAs) have been widely implemented for solving Multiobjective Optimization Problems (MOPs), the convergence of EAs towards Pareto optimal front is still an issue of concern. In order to enhance the robustness of EAs, hybrid algorithms are commonly developed to identify better solutions for MOPs. The prime focus of this research is placed on the integration of new proposed elitism in conventional Micro Genetic Algorithm (MGA). The proposed elitism has been studied in this research to develop Improved Micro Genetic Algorithm (IMGA). In this research, Kursawe and ZDT test functions are chosen as the benchmark studies for the assessment on IMGA. The accuracy and effectiveness of IMGA are evaluated based a number of quality indicators such as generational distance and non-dominated optimal spacing. The proposed IMGA is compared with Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), MGA and Fast Pareto Genetic Algorithm (FPGA). The assessment results show that IMGA can surpass the MGA in Kursawe test function by achieved 3.571E-4 for generational distance and 2.026E-2 for spacing. Meanwhile for ZDT benchmark, IMGA solved and suggested the optimal Pareto front for all the ZDT test functions. After having the benchmark evaluation, the proposed IMGA is applied to a practical case study on circuit design optimization. Two different circuit designs of active low pass filter that comprise of different number of input parameters are studied. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72831 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72831/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72831/1/Page%201-24.pdf 51537f895c9083bb4b75fbe7c192d70b http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72831/2/Full%20text.pdf 795cb02b4652df5e2f6887b81b21e411 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72831/4/Lim%20Wei%20Jer.pdf e9ca276c9a322b25b0901c83cb83c416 Universiti Malaysia Perlis (UniMAP) Evolutionary computation Computer algorithms Algorithms Evolutionary Algorithms (EAs) Multiobjective Optimization Problems (MOPs) School of Microelectronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Asral, Bahari Jambek, Dr.
topic	Evolutionary computation Computer algorithms Algorithms Evolutionary Algorithms (EAs) Multiobjective Optimization Problems (MOPs)
spellingShingle	Evolutionary computation Computer algorithms Algorithms Evolutionary Algorithms (EAs) Multiobjective Optimization Problems (MOPs) Improve micro genetic algorithm for multiobjective kursawe function and low pass filter circuit design optimization
description	Although Evolutionary Algorithms (EAs) have been widely implemented for solving Multiobjective Optimization Problems (MOPs), the convergence of EAs towards Pareto optimal front is still an issue of concern. In order to enhance the robustness of EAs, hybrid algorithms are commonly developed to identify better solutions for MOPs. The prime focus of this research is placed on the integration of new proposed elitism in conventional Micro Genetic Algorithm (MGA). The proposed elitism has been studied in this research to develop Improved Micro Genetic Algorithm (IMGA). In this research, Kursawe and ZDT test functions are chosen as the benchmark studies for the assessment on IMGA. The accuracy and effectiveness of IMGA are evaluated based a number of quality indicators such as generational distance and non-dominated optimal spacing. The proposed IMGA is compared with Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), MGA and Fast Pareto Genetic Algorithm (FPGA). The assessment results show that IMGA can surpass the MGA in Kursawe test function by achieved 3.571E-4 for generational distance and 2.026E-2 for spacing. Meanwhile for ZDT benchmark, IMGA solved and suggested the optimal Pareto front for all the ZDT test functions. After having the benchmark evaluation, the proposed IMGA is applied to a practical case study on circuit design optimization. Two different circuit designs of active low pass filter that comprise of different number of input parameters are studied.
format	Thesis
title	Improve micro genetic algorithm for multiobjective kursawe function and low pass filter circuit design optimization
title_short	Improve micro genetic algorithm for multiobjective kursawe function and low pass filter circuit design optimization
title_full	Improve micro genetic algorithm for multiobjective kursawe function and low pass filter circuit design optimization
title_fullStr	Improve micro genetic algorithm for multiobjective kursawe function and low pass filter circuit design optimization
title_full_unstemmed	Improve micro genetic algorithm for multiobjective kursawe function and low pass filter circuit design optimization
title_sort	improve micro genetic algorithm for multiobjective kursawe function and low pass filter circuit design optimization
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Microelectronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72831/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72831/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72831/4/Lim%20Wei%20Jer.pdf
_version_	1747836876672204800

Improve micro genetic algorithm for multiobjective kursawe function and low pass filter circuit design optimization

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