03120nam a2200337 i 4500003000800000005001700008006001900025007000300044008004100047020003300088040002300121100002900144245009600173264002400269264001200293300005500305336002100360336002100381337002500402337002300427338002300450338002800473347002400501500002500525502007300550504003900623520200900662610004002671650004502711650002602756MY-KuUP20251125110740.0a||||fr|||| 000 0 ta230614t20232023my a|||fr|||| 000 0 eng d  aTHE0009473 (Local)qHardback  aUMPbengcUMPerda1 aMd Abdul Kader,eauthor.10aFuzzy adaptive emperor penguin optimizer for global optimization problems /cMd Abdul Kader 1aPahang:bUMP,c2023 4c© 2023  axv, 157 pages :e1 CD ROMbIllustration ;c30 cm.+  2rdacontentatext  2rdacontentatext  2rdamediaaunmediated  2rdamediaacomputer  2rdacarrieravolume  2computeracomputer disc  2rdaatext filebPDF  aFaculty of Computing  aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2023  aIncludes bibliographical reference3 aThe Emperor Penguin Optimizer (EPO) is a recently developed population-based metaheuristic algorithm that simulates the huddling behaviour of emperor penguins. Mixed results have been observed in the performance of EPO in solving general optimization problems. Within the EPO, two parameters need to be tuned (namely f and l) to ensure a good balance between exploration (i.e., roaming unknown locations) and exploitation (i.e., manipulating the current known best). Since the search contour varies depending on the optimization problem, the tuning of parameters f and l is problem-dependent, and there is no one-size-fits-all approach. To alleviate this parameter tuning problem, an adaptive mechanism can be introduced in EPO. This research work proposes a fuzzy adaptive variant of EPO, namely FAEPO, to solve this problem. As the name suggests, FAEPO can adaptively tune the parameters f and l throughout the search based on three measures (i.e., quality, success rate, and diversity of the current search) via fuzzy decisions. A test suite of twelve benchmark test functions and three global optimization problems: Team Formation Optimization (TFO), Low Autocorrelation Binary Sequence (LABS), and Modified Condition/ Decision coverage (MC/DC) test case generation problem were solved using the proposed algorithm. The respective solution results of the competing metaheuristic algorithms were compared. The experimental results demonstrate that FAEPO significantly improved the performance especially of its predecessor (EPO), an improved variant of EPO (i.e., IEPO), and a fuzzy-based variant of ChOA (i.e., FChOA) and gives superior performance against the competing metaheuristic algorithms. Moreover, the proposed FAEPO requires 50% less fitness function evaluation in each iteration than the ancestor EPO and exhibits competitive performance in terms of convergence and computational time against its predecessor (EPO) and other competing meta-heuristic algorithms with a 90% confidence level.20aFaculty of ComputingxDissertations 0aUniversities and collegesxDissertations 0aThesisxDissertations