M. Fatih TasgetirenQuanke PanPonnuthurai Nagaratnam SuganthanAngela Hsiang Ling Chen2025-10-062010978142446910910.1109/CEC.2010.5586300https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959466769&doi=10.1109%2FCEC.2010.5586300&partnerID=40&md5=48b29961554b5650bf32cd8c1b89b939https://gcris.yasar.edu.tr/handle/123456789/10270Very recently Jarboui et al. [1] (Computers & Operations Research 36 (2009) 2638-2646) and Tseng and Lin [2] (European Journal of Operational Research 198 (2009) 84-92) presented a novel estimation distribution algorithm (EDA) and a hybrid genetic local search (hGLS) algorithm for the permutation flowshop scheduling (PFSP) with the total flowtime (TFT) criterion respectively. Both algorithms generated excellent results thus improving all the best known solutions reported in the literature so far. However in this paper we present a discrete artificial bee colony (DABC) algorithm hybridized with an iterated greedy (IG) and iterated local search (ILS) algorithms embedded in a variable neighborhood search (VNS) procedure based on swap and insertion neighborhood structures. We also present a hybrid version of our previous discrete differential evolution (hDDE) algorithm employing the IG and VNS structure too. The performance of the DABC and hDDE is highly competitive to the EDA and hGLS algorithms in terms of both solution quality and CPU times. Ultimately 43 out of 60 best known solutions provided very recently by the EDA and hGLS algorithms are further improved by the DABC and hDDE algorithms with short-term search. © 2010 IEEE. © 2011 Elsevier B.V. All rights reserved.EnglishArtificial Bee Colonies, Cpu Time, Differential Evolution, Distribution Algorithms, Hybrid Genetic, Iterated Local Search, Neighborhood Structure, Operational Research, Permutation Flow-shop Scheduling, Solution Quality, Total Flowtime, Variable Neighborhood Search, Artificial Intelligence, Biology, Evolutionary AlgorithmsArtificial bee colonies, CPU time, Differential Evolution, Distribution algorithms, Hybrid genetic, Iterated local search, Neighborhood structure, Operational research, Permutation flow-shop scheduling, Solution quality, Total flowtime, Variable neighborhood search, Artificial intelligence, Biology, Evolutionary algorithmsConference Object