Quan-Ke PanP. N. SuganthanM. Fatih TasgetirenJ. J. LiangLiang, J. J.Suganthan, P. N.Tasgetiren, M. FatihPan, Quan-Ke2025-10-0620100096-30031873-564910.1016/j.amc.2010.01.0882-s2.0-77949490771http://dx.doi.org/10.1016/j.amc.2010.01.088https://gcris.yasar.edu.tr/handle/123456789/7751https://doi.org/10.1016/j.amc.2010.01.088This paper presents a self-adaptive global best harmony search (SGHS) algorithm for solving continuous optimization problems. In the proposed SGHS algorithm a new improvisation scheme is developed so that the good information captured in the current global best solution can be well utilized to generate new harmonies. The harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR) are dynamically adapted by the learning mechanisms proposed. The distance bandwidth (BW) is dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from literature. The computational results show that the proposed SGHS algorithm is more effective in finding better solutions than the state-of-the-art harmony search (HS) variants. (C) 2010 Elsevier Inc. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessHarmony search, Evolutionary algorithms, Meta-heuristics, Continuous optimizationENGINEERING OPTIMIZATION, STRUCTURAL OPTIMIZATION, HEURISTIC ALGORITHM, OPTIMUM DESIGNHarmony SearchContinuous OptimizationMeta-heuristicsEvolutionary AlgorithmsArticle