000			03115ntm a2200337 i 4500
003			MY-KuUP
005			20260403161604.0
006			t||||fr|||| 000 0
007			ta
008			260403t20252025my a|||fr|||| 000 0 eng d
020			_aTHE0010431 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFKOM .A89 2025 r Thesis
100	1		_aNurul Asyikin Binti Zainal, _eauthor.
245	1	0	_aEnhancing the symbiotic organisms search algorithm with fuzzy adaptive selection / _cNurul Asyikin Binti Zainal
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		4	_c© 2025
300			_axv, 182 pages : _billustrations ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Computing
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang Al Sultan Abdullah - 2025
504			_aInclude bibliographical reference
520	3		_aThe Symbiotic Organism Search (SOS) algorithm has recently attracted significant attention due to its superior performance compared to other metaheuristic algorithms. A key advantage of SOS is its reliance on three distinct search phases (based on three update operators) and does not rely on any parameter controls. On the one hand, the three update operators work well (i.e., mutualism, commensalism, and parasitism) among each other and relieve the need to do significant tuning of any control parameters. On the other hand, not having any control parameters can also be a potential drawback. In the current form, SOS has a pre-set sequence for exploration (searching through the entire solution space) and exploitation (utilizing the known local best) beginning from commensalism, mutualism, and parasitism update operators. Having a pre-set sequence can be counter-productive because the search process becomes insensitive to the current needs of the optimization, which could lead to premature convergence or getting trapped in local optima by not adapting dynamically to the problem's requirements. To address these issues, this thesis proposes an adaptive fuzzy-based SOS variant called Fuzzy Symbiotic Optimization Search (FSOS). Based on the use of Mamdani fuzzy, FSOS can select one or a combination of the mutualism, commensalism, and parasitism update operators by adjusting their individual probabilities through fuzzy decision-making. FSOS was tested on 23 benchmark functions and applied to a t-way test generation case study to assess its effectiveness. The experimental results show that FSOS delivers competitive performance, surpassing its predecessor (SOS) and other metaheuristic algorithms with a 95% confidence level. Overall, the findings demonstrate the potential of a parameter-free adaptive algorithm in solving general optimization problems, making them relevant for scientific research and practical applications.
610	2	0	_aFaculty of Computing _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c104592 _d104598