03217nam a2200373 i 4500 0 0 lcc 0 1 REF 20000 20000 2023-06-14 0 FKOM .K33 2023 r Thesis T000002380 2023-06-14 00:00:00 1 2023-06-14 THESIS 0 0 lcc 0 0 REF 20000 20000 2023-06-14 0 CD13355 T000002381 2023-06-14 00:00:00 1 2023-06-14 THESIS 99549 99555 MY-KuUP 20251125110740.0 a||||fr|||| 000 0 ta 230614t20232023my a|||fr|||| 000 0 eng d THE0009473 (Local) Hardback UMP eng UMP rda FKOM .K33 2023 r Thesis Md Abdul Kader, author. Fuzzy adaptive emperor penguin optimizer for global optimization problems / Md Abdul Kader Pahang: UMP, 2023 © 2023 xv, 157 pages : 1 CD ROM Illustration ; 30 cm.+ rdacontent text rdacontent text rdamedia unmediated rdamedia computer rdacarrier volume computer computer disc rda text file PDF Faculty of Computing Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2023 Includes bibliographical reference The 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. Faculty of Computing Dissertations Universities and colleges Dissertations Thesis Dissertations lcc THESIS