Browsing by Author "Suganthan, P. N."

Now showing 1 - 12 of 12

Citation - WoS: 3
Citation - Scopus: 1
A differential evolution algorithm for the median cycle problem
(IEEE, 2011) M. Fatih Tasgetiren; Quanke Pan; Önder Bulut; Ponnuthurai Nagaratnam Suganthan; Tasgetiren, M. Fatih; Suganthan, P. N.; Pan, Quan-Ke; Bulut, Onder; Fadiloglu, M. Murat
This paper extends the applications of differential evolution algorithms to the Median Cycle Problem. The median cycle problem is concerned with constructing a simple cycle composed of a subset of vertices of a mixed graph. The objective is to minimize the cost of the cycle and the cost of assigning vertices not on the cycle to the nearest vertex on the cycle. A unique solution representation is presented for the differential evolution algorithm in order to solve the median cycle problem. To the best of our knowledge this is the first reported application of differential evolution algorithms to the median cycle problem in the literature. No local search is employed in order to see the performance of the pure differential evolution algorithm. The differential evolution algorithm is tested on a set of benchmark instances from the literature. For comparisons a continuous genetic algorithm is also developed. The computational results show that the differential evolution algorithm was superior to the genetic algorithm. In addition the computational results also show that the differential evolution algorithm is very promising in solving the median cycle problem when compared to the best performing algorithms from the literature. Ultimately given the fact that no local search is employed the DE algorithm was able to further improve the 5 out of 20 instances. © 2011 IEEE. © 2011 Elsevier B.V. All rights reserved.
Citation - WoS: 47
Citation - Scopus: 52
A differential evolution algorithm for the no-idle flowshop scheduling problem with total tardiness criterion
(Taylor & Francis Ltd, 2011) M. Fatih Tasgetiren; Quanke Pan; Ponnuthurai Nagaratnam Suganthan; Tay Jin Chua; Tasgetiren, M. Fatih; Suganthan, P. N.; Jin Chua, Tay; Pan, Quan-Ke; Chua, Tay Jin
In this paper we investigate the use of a continuous algorithm for the no-idle permutation flowshop scheduling (NIPFS) problem with tardiness criterion. For this purpose a differential evolution algorithm with variable parameter search (vpsDE) is developed to be compared to a well-known random key genetic algorithm (RKGA) from the literature. The motivation is due to the fact that a continuous DE can be very competitive for the problems where RKGAs are well suited. As an application area we choose the NIPFS problem with the total tardiness criterion in which there is no literature on it to the best of our knowledge. The NIPFS problem is a variant of the well-known permutation flowshop (PFSP) scheduling problem where idle time is not allowed on machines. In other words the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions. First of all a continuous optimisation algorithm is used to solve a combinatorial optimisation problem where some efficient methods of converting a continuous vector to a discrete job permutation and vice versa are presented. These methods are not problem specific and can be employed in any continuous algorithm to tackle the permutation type of optimisation problems. Secondly a variable parameter search is introduced for the differential evolution algorithm which significantly accelerates the search process for global optimisation and enhances the solution quality. Thirdly some novel ways of calculating the total tardiness from makespan are introduced for the NIPFS problem. The performance of vpsDE is evaluated against a well-known RKGA from the literature. The computational results show its highly competitive performance when compared to RKGA. It is shown in this paper that the vpsDE performs better than the RKGA thus providing an alternative solution approach to the literature that the RKGA can be well suited. © 2011 Taylor & Francis. © 2011 Elsevier B.V. All rights reserved.
Citation - WoS: 10
Citation - Scopus: 23
A Differential Evolution Algorithm with Q-Learning for Solving Engineering Design Problems
(Institute of Electrical and Electronics Engineers Inc., 2020) Damla Kizilay; M. Fatih Tasgetiren; Hande Oztop; Levent Kandiller; Ponnuthurai Nagaratnam Suganthan; Kizilay, Damla; Tasgetiren, M. Fatih; Suganthan, P. N.; Oztop, Hande; Kandiller, Levent
In this paper a differential evolution algorithm with Q-Learning (DE-QL) for solving engineering Design Problems (EDPs) is presented. As well known the performance of a DE algorithm depends on the mutation strategy and its control parameters namely crossover and mutation rates. For this reason the proposed DE-QL generates the trial population by using the QL method in such a way that the QL guides the selection of the mutation strategy amongst four distinct strategies as well as crossover and mutation rates from the Q table. The DE-QL algorithm is well equipped with the epsilon constraint handling method to balance the search between feasible regions and infeasible regions during the evolutionary process. Furthermore a new mutation operator namely DE/Best to current/l is proposed in the DE-QL algorithm. In this paper 57 EDPs provided in 'Problem Definitions and Evaluation Criteria for the CEC 2020 Competition and Special Session on A Test-suite of Non-Convex Constrained optimization Problems from the Real-World and Some Baseline Results' are tested by the DE-QL. We provide our results in Appendixes and will be evaluated with other competitors in the competition. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 479
Citation - Scopus: 583
A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem
(ELSEVIER SCIENCE INC, 2011) Quan-Ke Pan; M. Fatih Tasgetiren; P. N. Suganthan; T. J. Chua; Tasgetiren, M. Fatih; Suganthan, P. N.; Fatih Tasgetiren, M.; Pan, Quan-Ke; Chua, T. J.
In this paper a discrete artificial bee colony (DABC) algorithm is proposed to solve the lot-streaming flow shop scheduling problem with the criterion of total weighted earliness and tardiness penalties under both the idling and no-idling cases. Unlike the original ABC algorithm the proposed DABC algorithm represents a food source as a discrete job permutation and applies discrete operators to generate new neighboring food sources for the employed bees onlookers and scouts. An efficient initialization scheme which is based on the earliest due date (EDD) the smallest slack time on the last machine (LSL) and the smallest overall slack time (OSL) rules is presented to construct the initial population with certain quality and diversity. In addition a self adaptive strategy for generating neighboring food sources based on insert and swap operators is developed to enable the DABC algorithm to work on discrete/combinatorial spaces. Furthermore a simple but effective local search approach is embedded in the proposed DABC algorithm to enhance the local intensification capability. Through the analysis of experimental results the highly effective performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature. (C) 2010 Elsevier Inc. All rights reserved.
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.
Citation - WoS: 89
Citation - Scopus: 102
A variable iterated greedy algorithm with differential evolution for the no-idle permutation flowshop scheduling problem
(PERGAMON-ELSEVIER SCIENCE LTD, 2013) M. Fatih Tasgetiren; Quan-Ke Pan; P. N. Suganthan; Ozge Buyukdagli; Tasgetiren, M. Fatih; Suganthan, P. N.; Buyukdagli, Ozge; Fatih Tasgetiren, M.; Pan, Quan-Ke
This paper presents a variable iterated greedy algorithm (IG) with differential evolution (vIG_DE) designed to solve the no-idle permutation flowshop scheduling problem. In an IG algorithm size d of jobs are removed from a sequence and re-inserted into all possible positions of the remaining sequences of jobs which affects the performance of the algorithm. The basic concept behind the proposed vIG_DE algorithm is to employ differential evolution (DE) to determine two important parameters for the IG algorithm which are the destruction size and the probability of applying the IG algorithm to an individual. While DE optimizes the destruction size and the probability on a continuous domain by using DE mutation and crossover operators these two parameters are used to generate a trial individual by directly applying the IG algorithm to each target individual depending on the probability. Next the trial individual is replaced with the corresponding target individual if it is better in terms of fitness. A unique multi-vector chromosome representation is presented in such a way that the first vector represents the destruction size and the probability which is a DE vector whereas the second vector simply consists of a job permutation assigned to each individual in the target population. Furthermore the traditional IG and a variable IG from the literature are re-implemented as well. The proposed algorithms are applied to the no-idle permutation flowshop scheduling (NIPFS) problem with the makespan and total flowtime criteria. The performances of the proposed algorithms are tested on the Ruben Ruiz benchmark suite and compared to the best-known solutions available at http://soa.iti.es/rruiz as well as to those from a recent discrete differential evolution algorithm (HDDE) from the literature. The computational results show that all three IG variants represent state-of-art methods for the NIPFS problem. (C) 2013 Elsevier Ltd. All rights reserved.
Citation - WoS: 5
Citation - Scopus: 5
An Ensemble of Differential Evolution Algorithms with Variable Neighborhood Search for Constrained Function Optimization
(IEEE, 2016) Mert Paldrak; M. Fatih Tasgetiren; P. N. Suganthan; Quan-Ke Pan; Tasgetiren, M. Fatih; Suganthan, P. N.; Paldrak, Mert; Pan, Quan-Ke
In this paper an ensemble of differential evolution algorithms based on a variable neighborhood search algorithm (EDE-VNS) is proposed so as to solve the constrained real parameter-optimization problems. The performance of DE algorithms heavily depends on the mutation strategies crossover operators and control parameters employed. The proposed EDEVNS algorithm employs multiple mutation operators and control parameters in its VNS loops to enhance the solution quality. In addition we utilize opposition-based learning (OBL) to take advantages of opposite solutions to find a candidate solution which might be close to the global optimum. In addition we also present an idea of injecting some good dimensional values from promising areas in the population to the trial individual through the injection procedure. The computational results show that the EDE-VNS algorithm is very competitive to some of the best performing algorithms from the literature.
Citation - WoS: 1176
Citation - Scopus: 1308
Differential evolution algorithm with ensemble of parameters and mutation strategies
(Elsevier, 2011) Rammohan Mallipeddi; Ponnuthurai Nagaratnam Suganthan; Quanke Pan; M. Fatih Tasgetiren; Mallipeddi, R.; Suganthan, P. N.; Tasgetiren, M. F.; Pan, Q. K.
Differential evolution (DE) has attracted much attention recently as an effective approach for solving numerical optimization problems. However the performance of DE is sensitive to the choice of the mutation strategy and associated control parameters. Thus to obtain optimal performance time-consuming parameter tuning is necessary. Different mutation strategies with different parameter settings can be appropriate during different stages of the evolution. In this paper we propose to employ an ensemble of mutation strategies and control parameters with the DE (EPSDE). In EPSDE a pool of distinct mutation strategies along with a pool of values for each control parameter coexists throughout the evolution process and competes to produce offspring. The performance of EPSDE is evaluated on a set of bound-constrained problems and is compared with conventional DE and several state-of-the-art parameter adaptive DE variants. © 2010 Elsevier B.V. All rights reserved. © 2011 Elsevier B.V. All rights reserved.
Citation - WoS: 13
Citation - Scopus: 3
Metaheuristic Algorithms for the Quadratic Assignment Problem
(IEEE, 2013) M. Fatih Tasgetiren; Quan-Ke Pan; P. N. Suganthan; Ikbal Ece Dizbay; Tasgetiren, M. Fatih; Suganthan, P. N.; Oz, Dindar; Pan, Quan-Ke; Dizbay, Ikbal Ece; Türkkahraman, Şeyda Melis
This paper presents two meta-heuristic algorithms to solve the quadratic assignment problem. The iterated greedy algorithm has two main components hich are destruction and construction procedures. The algorithm starts from an initial solution and then iterates through a main loop where first a partial candidate solution is obtained by removing a number of solution components from a complete candidate solution. Then a complete solution is reconstructed by inserting the partial solution components in the destructed solution. These simple steps are iterated until some predetermined termination criterion is met. We also present our previous discrete differential evolution algorithm modified for the quadratic assignment problem. The quadratic assignment problem is a classical NP-hard problem and its applications in real life are still considered challenging. The proposed algorithms were evaluated on quadratic assignment problem instances arising from real life problems as well as on a number of benchmark instances from the QAPLIB. The computational results show that the proposed algorithms are superior to the migrating birds optimization algorithm which appeared very recently in the literature. Ultimately 7 out of 8 printed circuit boards (PCB) instances are further improved.
Citation - WoS: 2
Citation - Scopus: 4
Multi-objective harmony search algorithm for layout design in theatre hall acoustics
(Institute of Electrical and Electronics Engineers Inc., 2016) Cemre Cubukcuoglu; Ayca Kirimtat; M. Fatih Tasgetiren; Ponnuthurai Nagaratnam Suganthan; Quanke Pan; Tasgetiren, M. Faith; Suganthan, P. N.; Cubukcuoglu, Cemre; Pan, Quan-Ke; Kirimtat, Ayca
The aim of the research is to find a feasible set of theatre hall design alternatives for two objectives which are the total cost and the reverberation time subject to several constraints. We formulate the problem as a multi-objective realparameter constrained optimization problem. To handle this problem we investigated two different optimization algorithms namely a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and a multi-objective Harmony Search algorithm (MOHS) in order to gather Pareto front approximation with a set of non-dominated solutions. We demonstrate that the MOHS yields slightly better results than the NSGA-II algorithm. © 2017 Elsevier B.V. All rights reserved.