Publication: Experiments with New Stochastic Global Optimization Search Techniques
PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
In 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.
probabilistic search methods, global optimization, adaptive partitioning algorithms, fuzzy measures, olasılıklı arama yöntemleri, global optimizasyon, adaptif bölümleme algoritmaları, bulanık önlemler