Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
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Browsing Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering by Subject "adaptive partitioning algorithms"
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Publication A Note On the Use of a Fuzzy Approach in Adaptive Partitioning Algorithms for Global Optimization(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA, 1999-08) Demirhan, Melek; Özdamar, LinetIn global optimization, adaptive partitioning algorithms (APA) operate on the basis of partitioning the feasible region into subregions, sampling and evaluating each subregion, and selecting one or more subregions for repartitioning, The purpose of the repartitioning process is to locate a narrow neighborhood around the global optimum. In this correspondence, He propose to use a fuzzy approach in the assessment of subregions using random samples taken from these subregions. We discuss different types of uncertainties involved in APA and ne conclude that the use of a fuzzy approach in the assessment of subregions is in concurrence with APA's convergence property, We provide numerical results for the fuzzy approach on 13 test functions from the literature.Publication Comparison of Partition Evaluation Measures in an Adaptive Partitioning Algorithm for Global Optimization(ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS, 2001-01-01) Özdamar, Linet; Demirhan, MAn adaptive partitioning algorithm with random search is proposed to locate the global optimum of multimodal functions. Partitioning algorithms divide the feasible region into nonoverlapping partitions in order to restrict and direct the search to the most promising region expected to contain the global optimum. In such a scheme a partition evaluation measure is required to assess sub-regions in order to re-partition the most promising sub-region and intensify the search within that area. This study provides computational results on several classes of partition evaluation measures used in the assessment of samples taken from all partitions. Among the partition evaluation classes used in our comparison are fuzzy, statistical, and deterministic interval estimation measures. Performance in terms of solution quality is reported on an extensive set of 77 test functions collected from the literature.Publication Experiments with New Stochastic Global Optimization Search Techniques(PERGAMON-ELSEVIER SCIENCE LTD., 2000-08) ÖZDAMAR, LİNET; Demirhan, MelekIn this paper several probabilistic search techniques are developed for global optimization under three heuristic classifications: simulated annealing, clustering methods and adaptive partitioning algorithms. The algorithms proposed here combine different methods found in the literature and they are compared with well-established approaches in the corresponding areas. Computational results are obtained on 77 small to moderate size (up to 10 variables) nonlinear test functions with simple bounds and Is large size test functions (up to 400 variables) collected from literature.