Details

Autor(en) / Beteiligte

• Georgieva, A.
• Jordanov, I.

Titel

Global optimization based on novel heuristics, low-discrepancy sequences and genetic algorithms

Ist Teil von

• European journal of operational research, 2009-07, Vol.196 (2), p.413-422

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2009

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• In this paper a new heuristic hybrid technique for bound-constrained global optimization is proposed. We developed iterative algorithm called GLP τ S that uses genetic algorithms, LP τ low-discrepancy sequences of points and heuristic rules to find regions of attraction when searching a global minimum of an objective function. Subsequently Nelder–Mead Simplex local search technique is used to refine the solution. The combination of the three techniques (Genetic algorithms, LP τ O Low-discrepancy search and Simplex search) provides a powerful hybrid heuristic optimization method which is tested on a number of benchmark multimodal functions with 10–150 dimensions, and the method properties – applicability, convergence, consistency and stability are discussed in detail.