03115ntm a2200337 i 4500 MY-KuUP 20260403161604.0 t||||fr|||| 000 0 ta 260403t20252025my a|||fr|||| 000 0 eng d THE0010431 (Local) Hardback UMPSA eng UMPSA rda FKOM .A89 2025 r Thesis Nurul Asyikin Binti Zainal, author. Enhancing the symbiotic organisms search algorithm with fuzzy adaptive selection / Nurul Asyikin Binti Zainal Kuantan, Pahang : UMPSA, 2025 © 2025 xv, 182 pages : illustrations ; 30 cm. + 1 CD-ROM rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Faculty of Computing Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang Al Sultan Abdullah - 2025 Include bibliographical reference The 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. Faculty of Computing Dissertations Universities and colleges Dissertations Theses Dissertations lcc THESIS 0 0 lcc 0 1 20000 20000 2026-04-03 0 FKOM .A89 2025 r Thesis T000004756 2026-04-03 16:16:14 2026-04-03 THESIS 0 0 lcc 0 6 20000 20000 2026-04-03 0 CD13979 T000004757 2026-04-03 16:16:33 2026-04-03 THESIS 104592 104598