Browsing by Author "Liang, J. J."

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Citation - WoS: 88
Citation - Scopus: 105
A local-best harmony search algorithm with dynamic sub-harmony memories for lot-streaming flow shop scheduling problem
(Pergamon-Elsevier Science Ltd, 2011) Quanke Pan; Ponnuthurai Nagaratnam Suganthan; Jing Liang; M. Fatih Tasgetiren; Liang, J. J.; Suganthan, P. N.; Tasgetiren, M. Fatih; Pan, Quan-Ke
In this paper a local-best harmony search (HS) algorithm with dynamic sub-harmony memories (HM) namely DLHS algorithm is proposed to minimize the total weighted earliness and tardiness penalties for a lot-streaming flow shop scheduling problem with equal-size sub-lots. First of all to make the HS algorithm suitable for solving the problem considered a rank-of-value (ROV) rule is applied to convert the continuous harmony vectors to discrete job sequences and a net benefit of movement (NBM) heuristic is utilized to yield the optimal sub-lot allocations for the obtained job sequences. Secondly an efficient initialization scheme based on the NEH variants is presented to construct an initial HM with certain quality and diversity. Thirdly during the evolution process the HM is dynamically divided into many small-sized sub-HMs which evolve independently so as to balance the fast convergence and large diversity. Fourthly a new improvisation scheme is developed to well inherit good structures from the local-best harmony vector in the sub-HM. Meanwhile a chaotic sequence to produce decision variables for harmony vectors and a mutation scheme are utilized to enhance the diversity of the HM. In addition a simple but effective local search approach is presented and embedded in the DLHS algorithm to enhance the local searching ability. Computational experiments and comparisons show that the proposed DLHS algorithm generates better or competitive results than the existing hybrid genetic algorithm (HGA) and hybrid discrete particle swarm optimization (HDPSO) for the lot-streaming flow shop scheduling problem with total weighted earliness and tardiness criterion. © 2010 Elsevier Ltd. All rights reserved. © 2011 Elsevier B.V. All rights reserved.
Citation - WoS: 62
Citation - Scopus: 72
A local-best harmony search algorithm with dynamic subpopulations
(TAYLOR & FRANCIS LTD, 2010) Quan-Ke Pan; P. N. Suganthan; J. J. Liang; M. Fatih Tasgetiren; Liang, J. J.; Suganthan, P. N.; Tasgetiren, M. Fatih; Pan, Quan-Ke
This article presents a local-best harmony search algorithm with dynamic subpopulations (DLHS) for solving the bound-constrained continuous optimization problems. Unlike existing harmony search algorithms the DLHS algorithm divides the whole harmony memory (HM) into many small-sized sub-HMs and the evolution is performed in each sub-HM independently. To maintain the diversity of the population and to improve the accuracy of the final solution information exchange among the sub-HMs is achieved by using a periodic regrouping schedule. Furthermore a novel harmony improvisation scheme is employed to benefit from good information captured in the local best harmony vector. In addition an adaptive strategy is developed to adjust the parameters to suit the particular problems or the particular phases of search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from the literature. The computational results show that overall the proposed DLHS algorithm is more effective or at least competitive in finding near-optimal solutions compared with state-of-the-art harmony search variants.
Citation - WoS: 298
Citation - Scopus: 384
A self-adaptive global best harmony search algorithm for continuous optimization problems
(ELSEVIER SCIENCE INC, 2010) Quan-Ke Pan; P. N. Suganthan; M. Fatih Tasgetiren; J. J. Liang; Liang, J. J.; Suganthan, P. N.; Tasgetiren, M. Fatih; Pan, Quan-Ke
This 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.