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008			251224b |||||||| |||| 00| 0 eng d
020			_aTHE0010180 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKPM .I34 2025 r Thesis
100	1		_aNur Iffah Binti Mohamed Azmi, _eauthor.
245	1	0	_aNovel particle swarm optimisation approach integrating lévy flight and doppler effect for pid parameter optimisation in dc motor systems / _cNur Iffah Binti Mohamed Azmi
264		1	_aKuantan, Pahang : _bUMPSA , _c2025
264		4	_c© 2025
300			_axviii, 192 pages : _billustrations ;illustrations ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Manufacturing & Mechatronics Engineering Technology
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2025
504			_aIncludes bibliographical references
520	3		_aOptimisation plays a critical role in solving complex engineering problems, particularly within constrained optimisation and control system tuning domains. This research proposes an enhanced swarm intelligence algorithm (Particle Swarm Optimisation with Lévy Flight and Doppler Effect (PSO-LFDE)) designed to address the limitations of classical PSO by improving convergence velocity, solution quality, and robustness in high-dimensional, multimodal search landscapes. The proposed algorithm incorporates Lévy Flight (LF) to facilitate adaptive, long-distance position updates for superior global exploration, and integrates a Doppler Effect (DE)-inspired mechanism to dynamically modulate inertia weights, enhancing the balance between exploration and exploitation throughout the search process. The algorithm’s efficacy is evaluated through a comprehensive set of six constrained optimisation benchmark functions and a mechanical design problem (the speed reducer design) widely used in engineering optimisation research. PSO-LFDE’s performance is benchmarked against 13 state-of-the-art algorithms including α-Simplex, ASCHEA, CRGAL, CULDEV, DSS-MDEV2, DEVLC, HEA-ACT, ISR, MBA, PSO-DEV, SAPF, SMES, and SR. For the speed reducer optimisation, comparative analysis is performed against DSS-MDEV2, DEVLC, HEA-ACT, MBA, PSO-DEV, mbDEV, and the Society and Civilisation Algorithm (SC). Simulations are implemented in MATLAB R2017a, with real-time validation via Arduino-based PID-controlled DC motor hardware to assess practical feasibility. Results demonstrate that PSO-LFDE consistently achieves superior best and mean fitness values, significantly reducing standard deviation across all benchmark functions, indicating enhanced stability. In the speed reducer design task, PSO-LFDE yields the most optimal parameter configuration. Mean Absolute Error (MAE) analysis further validates the algorithm’s accuracy, with PSO-LFDE ranking first among 14 algorithms on benchmark functions (MAE = -893.4400), and first among 8 on engineering design problems (MAE = -631.0300), surpassing algorithms such as DSS-MDEV2 and HEA-ACT. In control system tuning, PSO-LFDE enhances the dynamic performance of a DC motor, achieving up to 86.78% faster settling time, 70.60% reduction in peak time, and lower steady-state error compared to conventional PSO. Across multiple set-point inputs for both position (10, 30, 60 cm) and speed (5, 10, 15 rpm) control, PSO-LFDE demonstrates consistently improved rise time, settling time, and overshoot reduction. The combination of swarm intelligence and physics-inspired strategies in PSO-LFDE establishes a promising framework for high-performance optimisation in constrained environments, with significant implications for engineering design, intelligent control, and real-time embedded systems.
610	2	0	_aFaculty of Manufacturing & Mechatronics Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aThesis _xDissertations
942			_2lcc _cTHESIS
999			_c103799 _d103805