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008			240605t20242024my a|||fr|||| 000 0 eng d
020			_aTHE0009876 (Local) _qHardback
040			_aUMPSA _beng _cUMP _erda
090			_aFTKEE .H45 2024 r Thesis
100	0		_aMohd Helmi Bin Suid, _eauthor.
245	1	0	_aHybridization of nonlinear sine cosine and safe experimentation dynamics algorithms for solving control engineering optimization problems / _cMohd Helmi Bin Suid,
260		1	_aKuantan, Pahang : _bUMPSA, _c2024
264		4	_c© 2024
300			_axiv, 212 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Electrical & Electronics Engineering Technology
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2024
504			_aIncludes bibliographical references
520	3		_aIn the rapidly developing landscape of control engineering within electrical and electronics engineering, the study addresses critical challenges posed by the escalating complexity of system designs. As technology continues to advance, the demand for effective methodologies to design, analyse, and synthesize complex system models becomes vital. In response to this imperative, researchers have increasingly turned to optimization-based approaches, with the Sine Cosine Algorithm (SCA) emerging as a prominent solution. However, existing limitations in terms of convergence accuracy and local optima stagnation have prompted the introduction of innovative improvements in this study. The Nonlinear Sine Cosine Algorithm (NSCA) is introduced as a key extension, incorporating a versatile nonlinear decreasing gain into the transition parameter mechanism. This enhancement offers a tailored balance between exploration and exploitation, aligning with the specific requirements of complex electrical and electronics engineering problems. Moreover, the Nonlinear Sine Cosine Algorithm-Safe Experimentation Dynamic (NSCA-SED) introduces a hybridization of multi-agent and single-agent algorithms, presenting a dynamic approach with random perturbation to navigate search trajectories effectively, and release design parameters that might be trapped in local optima. The empirical assessment of these proposed methods encompasses a diverse set of 23 benchmark functions, demonstrating their efficacy comparable to well-established metaheuristic algorithms such as as the Grey Wolf Optimizer (GWO), Multi-Verse Optimization (MVO), Sine Cosine Algorithm (SCA), Ant Lion Optimizer (ALO), Moth-Flame Optimization Algorithm (MFO), and Grasshopper Optimization Algorithm (GOA). The applications extend beyond established optimization problems, addressing contemporary experiments in control engineering, including Model Order Reduction, Nonlinear System Identification, and Data-driven Control. Simulation results underscore the robustness and superiority of the NSCA and NSCA-SED algorithms in these contexts, showcasing improvements ranging from 13.97% to 97.17% for Model Order Reduction, 17.76% to 99.37% for Nonlinear System Identification, and 84.51% to 89.47% for Data-driven Control when compared to the standard SCA. In summary, this study not only contributes advancements to optimization algorithms but also directly addresses and enhances methodologies in electrical and electronics engineering. By overcoming the limitations of existing approaches, the NSCA and NSCA-SED algorithms stand as valuable tools in the collection of control engineers, facilitating the design and optimization of complex systems in contemporary electrical and electronics applications.
610	2	0	_aFaculty of Electrical & Electronics Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS