A Hybrid Harmony Search Method Based on OBL
Computational Science and Engineering（2010）
摘要
The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and fails to find the global optima in an efficient way. In this paper, we propose and study a hybrid optimization approach, in which the HS is merged together with the Opposition-Based Learning (OBL). Our modified HS, namely HS-OBL, has an improved convergence property. Simulations of 23 typical benchmark problems demonstrate that the HS-OBL can indeed yield a superior optimization performance over the regular HS method.
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关键词
evolutionary computation technique,opposition-based learning,regular hs method,hybrid harmony search method,harmony search,superior optimization performance,meta-heuristic optimization algorithm,hybrid optimization approach,improved convergence property,global optimum,modified hs,convergence,ellipsoids,evolutionary computing,evolutionary computation,gallium,computational modeling,genetic algorithms
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