A hybrid meta-heuristic for global optimisation using low-discrepancy sequences of points

详细信息    查看全文

• 作者：Antoniya Georgieva ; Ivan Jordanov
• 关键词：Global optimisation ; Meta-heuristics ; Hybrid methods ; Local minima problem ; Low-discrepancy sequences
• 刊名：Computers and Operations Research
• 出版年：2010
• 出版时间：March 2010
• 年：2010
• 卷：37
• 期：3
• 页码：456-469
• 全文大小：1158 K

文摘

A hybrid novel meta-heuristic technique for bound-constrained global optimisation (GO) is proposed in this paper. We have developed an iterative algorithm called LPτ Optimisation (LPτO) that uses low-discrepancy sequences of points and meta-heuristic knowledge to find regions of attraction when searching for a global minimum of an objective function. Subsequently, the well-known Nelder–Mead (NM) simplex local search is used to refine the solution found by the LPτO method. The combination of the two techniques (LPτO and NM) provides a powerful hybrid optimisation technique, which we call LPτNM. Its properties—applicability, convergence, consistency and stability are discussed here in detail. The LPτNM is tested on a number of benchmark multimodal mathematical functions from 2 to 20 dimensions and compared with results from other stochastic heuristic methods.