Quanke PanPonnuthurai Nagaratnam SuganthanM. Fatih TasgetirenJing Liang2025-10-062010009630030096-300310.1016/j.amc.2010.01.088https://www.scopus.com/inward/record.uri?eid=2-s2.0-77949490771&doi=10.1016%2Fj.amc.2010.01.088&partnerID=40&md5=723501d9927b89221c7cfb8b2010e0cahttps://gcris.yasar.edu.tr/handle/123456789/10290This 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. © 2010 Elsevier Inc. All rights reserved. © 2010 Elsevier B.V. All rights reserved.EnglishContinuous Optimization, Evolutionary Algorithms, Harmony Search, Meta-heuristics, Bench-mark Problems, Computational Results, Computational Simulation, Continuous Optimization, Continuous Optimization Problems, Harmony Search, Harmony Search Algorithms, Learning Mechanism, Meta Heuristics, Search Process, Self-adaptive, Learning Algorithms, Optimization, Evolutionary AlgorithmsBench-mark problems, Computational results, Computational simulation, Continuous optimization, Continuous optimization problems, Harmony search, Harmony search algorithms, Learning mechanism, Meta heuristics, Search process, Self-adaptive, Learning algorithms, Optimization, Evolutionary algorithmsArticle