Browsing by Author "Suganthan, P. Nagaratnam"

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    Conference Object
    Citation - Scopus: 17
    A Discrete Artificial Bee Colony Algorithm for the Permutation Flow Shop Scheduling Problem with Total Flowtime Criterion
    (IEEE, 2010) M. Fatih Tasgetiren; Quan-Ke Pan; P. Nagaratnam Suganthan; Angela H-L Chen; Tasgetiren, M. Fatih; Suganthan, P. Nagaratnam; Karabulut, Korhan; Pan, Quan-Ke; Ince, Yavuz; Chen, Angela H.-L.; Wang, Ling
    Very 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.
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    Conference Object
    Citation - WoS: 18
    Citation - Scopus: 55
    An ensemble of differential evolution algorithms for constrained function optimization
    (IEEE, 2010) M. Fatih Tasgetiren; Ponnuthurai Nagaratnam Suganthan; Quanke Pan; Rammohan Mallipeddi; Sedat Sarman; Tasgetiren, M. Fatih; Suganthan, P. Nagaratnam; Mallipeddi, Rammohan; Pan, Quan-Ke; Sarman, Sedat
    This paper presents an ensemble of differential evolution algorithms employing the variable parameter search and two distinct mutation strategies in the ensemble to solve real-parameter constrained optimization problems. It is well known that the performance of DE is sensitive to the choice of mutation strategies and associated control parameters. For these reasons the ensemble is achieved in such a way that each individual is assigned to one of the two distinct mutation strategies or a variable parameter search (VPS). The algorithm was tested using benchmark instances in Congress on Evolutionary Computation 2010. For these benchmark problems the problem definition file codes and evaluation criteria are available in http://www.ntu.edu.sg/home/EPNSugan. Since the optimal or best known solutions are not available in the literature the detailed computational results required in line with the special session format are provided for the competition. © 2010 IEEE. © 2011 Elsevier B.V. All rights reserved.