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008			191106t20192019my a|||fram|| 000 0 eng d
020			_aTHE0008193(Local) _qhardback
040			_aUMP _beng _cUMP _erda
090			_aFKEE .A99 2019 r Thesis
100	0		_aAhmad Azwan Abdul Razak, _eauthor.
245	1	0	_aMulti-objective spiral dynamic algorithms-based for a better accuracy and diversity / _cAhmad Azwan Abdul Razak
264		1	_aKuantan, Pahang : _bUMP, _c2019
264		4	_c© 2019
300			_axiv, 156 pages : _billustrations ; _c30 cm. + _e1 CD ROM
336			_atext _2rdacontent
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aFaculty of Electrical and Electronics Engineering
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2019
504			_aIncludes bibliographical references
520	3		_aOptimization algorithm plays an important role in solving many complex and real-world problems. Solution offers by the algorithm has high accuracy and reliable. Moreover, with the fast development in computing technology, application of optimization algorithm in solving problems is easier and becomes more practical. Optimization algorithm is also known as a metaheuristic algorithm which is originally come from heuristic approach. It is a heuristic algorithm that is integrated with an advance strategy inspired from many natural phenomena found on earth. The algorithms can be categorized into a single objective and a multi-objective type. Single objective type optimization algorithm can be applied to solve a problem with a single objective. On the other hand, multi-objective algorithm is applicable to solve a problem with two or more objectives. A more complex problem in which has two conflicting objectives where it is not solvable by the single objective type algorithm is the right type of problem for the multi-objective algorithm. Solution produced by the multi-objective algorithm is always presented in Pareto front curve representation. The produced Pareto front curve is a measure of how good the solution produced by the algorithm is. The main measurement criteria include the accuracy of the solution to the actual Pareto curve and the distribution of the found solution on the actual Pareto curve. Yet the performance of many multi-objective algorithms in terms of the accuracy and the distributed solution is not achieved at the highest performance level. There are still rooms for improvement the algorithm performance by manipulating the algorithm strategy. This thesis presents two variants of multi-objective type algorithms based on a Spiral Dynamic Algorithm (SDA) with application to optimize a Proportional-Derivative (PD) controller for an Inverted Pendulum System. The first variant is known as a Nondominated Sorting Multi-objective Spiral Dynamic Algorithm (MOSDA-NS). It is a strategy which combines a Nondominated Sorting and Crowding distance approaches with the SDA. The second variant is known as Archived-based Multi-objective Spiral Dynamic Algorithm (MOSDA-A). It is a strategy combining an Archived approach with the SDA. All the developed algorithms were tested with a set of benchmark functions comprising of 10 different functions covering various fitness landscapes and features. Both accuracy performance and distribution of the found solution on the obtained Pareto front are recorded. A statistical analysis is then conducted via a Wilcoxon Sign Rank test and a Friedman test. Both tests are conducted to verify the significant improvement of the solution obtained via the proposed algorithms to the Multi-objective Particle Swarm Optimization (MOPSO) and Multi-objective Nondominated Sorting Genetic Algorithm II (NSGAII). Result from the test shows that the proposed MOSDA-NS achieved the best accuracy and distribution performances compared to all other algorithms. In terms of solving a real-world problem, the proposed algorithms are applied to optimize two different Proportional-Derivative (PD) controllers for an Inverted Pendulum system attached on a moving cart. The first PD controller attenuates error for a linear movement of the moving cart. The second PD controller eliminates error for a rotating angle of the inverted pendulum. Transient responses of both pendulum angle and cart position in time-domain representation are recorded. An analysis on the transient responses is then conducted which measuring steady-state error, percentage overshoot, rise time and settling time. Finding of the analysis indicates that the proposed algorithms have resulted in a better control performance compared to the MOPSO and NSGAII.
610	2	0	_aFaculty of Electrical and Electronics Engineering _xDissertations
650		0	_aUniversities and colleges _xDisertations
650		0	_aTheses
942			_2lcc _cTHESIS