Global optimization based on novel heuristics,low-discrepancy sequences and genetic algorithms |
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Authors: | A Georgieva I Jordanov |
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Institution: | School of Computing, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK |
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Abstract: | In this paper a new heuristic hybrid technique for bound-constrained global optimization is proposed. We developed iterative algorithm called GLPτS that uses genetic algorithms, LPτ 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, LPτO 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. |
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Keywords: | Global optimization Genetic algorithms Heuristics Low-discrepancy sequences Hybrid methods |
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