Browsing by Author "Suganthan, P.N."

Now showing 1 - 8 of 8

Citation - WoS: 8
Citation - Scopus: 9
A differential evolution algorithm with a variable neighborhood search for constrained function optimization
(Springer Verlag service@springer.de, 2015) M. Fatih Tasgetiren; Ponnuthurai Nagaratnam Suganthan; Sel Ozcan; Damla Kizilay; Tasgetiren, M. Fatih; Suganthan, P.N.; Kizilay, Damla; Ozcan, Sel
In this paper a differential evolution algorithm based on a variable neighborhood search algorithm (DE_VNS) is proposed in order to solve the constrained real-parameter optimization problems. The performance of DE algorithm depends on the mutation strategies crossover operators and control parameters. As a result a DE_VNS algorithm that can employ multiple mutation operators in its VNS loops is proposed in order to further enhance the solution quality. We also present an idea of injecting some good dimensional values to the trial individual through the injection procedure. In addition we also present a diversification procedure that is based on the inversion of the target individuals and injection of some good dimensional values from promising areas in the population by tournament selection. The computational results show that the simple DE_VNS algorithm was very competitive to some of the best performing algorithms from the literature. © 2015 Elsevier B.V. All rights reserved.
Citation - WoS: 82
Citation - Scopus: 103
A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion
(ELSEVIER SCIENCE INC, 2013) M. Fatih Tasgetiren; Quan-Ke Pan; P. N. Suganthan; Adalet Oner; Suganthan, P.N.; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Oner, Adalet; Pan, Quan-Ke
In this paper we present a discrete artificial bee colony algorithm to solve the no-idle permutation flowshop scheduling problem with the total tardiness criterion. The no-idle permutation flowshop problem is a variant of the well-known permutation flowshop 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 discrete artificial bee colony algorithm is presented to solve the problem on hand first time in the literature. Secondly some novel methods of calculating the total tardiness from make-span are introduced for the no-idle permutation flowshop scheduling problem. Finally the main contribution of the paper is due to the fact that a novel speed-up method for the insertion neighborhood is developed for the total tardiness criterion. The performance of the discrete artificial bee colony algorithm is evaluated against a traditional genetic algorithm. The computational results show its highly competitive performance when compared to the genetic algorithm. Ultimately we provide the best known solutions for the total tardiness criterion with different due date tightness levels for the first time in the literature for the Taillard's benchmark suit. (C) 2013 Elsevier Inc. All rights reserved.
Citation - WoS: 201
Citation - Scopus: 241
A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops
(Elsevier Science Inc, 2011) M. Fatih Tasgetiren; Quanke Pan; Ponnuthurai Nagaratnam Suganthan; Angela Hsiang Ling Chen; Tasgetiren, M. Fatih; Suganthan, P.N.; Pan, Quan-Ke; Chen, Angela H-L
Obtaining an optimal solution for a permutation flowshop scheduling problem with the total flowtime criterion in a reasonable computational timeframe using traditional approaches and optimization tools has been a challenge. This paper presents a discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flowtime. Iterated greedy algorithms are comprised of local search procedures based on insertion and swap neighborhood structures. In the same context we also consider a discrete differential evolution algorithm from our previous work. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the discrete artificial bee colony and hybrid differential evolution algorithms is compared against the best performing algorithms from the existing literature in terms of both solution quality and CPU times. Ultimately 44 out of the 90 best known solutions provided very recently by the best performing estimation of distribution and genetic local search algorithms are further improved by the proposed algorithms with short-term searches. The solutions known to be the best to date are reported for the benchmark suite of Taillard with long-term searches as well. © 2011 Elsevier Inc. All rights reserved. © 2011 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
A Variable Iterated Greedy Algorithm with Differential Evolution for Solving No-Idle Flowshops
(SPRINGER-VERLAG BERLIN, 2012) M. Fatih Tasgetiren; Quan-Ke Pan; P. N. Suganthan; Ozge Buyukdagli; Tasgetiren, M. Fatih; Suganthan, P.N.; Buyukdagli, Ozge; Pan, Quan-Ke; L Rutkowski; M Korytkowski; R Scherer; R Tadeusiewicz; LA Zadeh; JM Zurada
In this paper we present a variable iterated greedy algorithm where its parameters (basically destruction size and probability of whether or not to apply the iterated greedy algorithm to an individual) are optimized by the differential evolution algorithm. A unique multi-chromosome solution representation is presented in such a way that the first chromosome represents the destruction size and the probability whereas the second chromosome is simply a job permutation assigned to each individual in the population randomly. The proposed algorithm is applied to the no-idle permutation flowshop scheduling problem with the makespan criterion. The performance of the proposed algorithm is tested on the Ruben Ruiz's benchmark suite and compared to their best known solutions available in http://soa.iti.es/rruiz as well as to a very recent discrete differential evolution algorithm from the literature. The computational results show its highly competitive performance and ultimately 183 out of 250 instances are further improved. In comparison to the very recent hybrid discrete differential evolution algorithm 114 out of 150 new best known solutions they provided are also further improved.
Citation - WoS: 107
Citation - Scopus: 125
An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem
(Elsevier Science Inc, 2010) M. Fatih Tasgetiren; Ponnuthurai Nagaratnam Suganthan; Quanke Pan; Suganthan, P.N.; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Pan, Quan-Ke
In this paper an ensemble of discrete differential evolution algorithms with parallel populations is presented. In a single populated discrete differential evolution (DDE) algorithm the destruction and construction (DC) procedure is employed to generate the mutant population whereas the trial population is obtained through a crossover operator. The performance of the DDE algorithm is substantially affected by the parameters of DC procedure as well as the choice of crossover operator. In order to enable the DDE algorithm to make use of different parameter values and crossover operators simultaneously we propose an ensemble of DDE (eDDE) algorithms where each parameter set and crossover operator is assigned to one of the parallel populations. Each parallel parent population does not only compete with offspring population generated by its own population but also the offspring populations generated by all other parallel populations which use different parameter settings and crossover operators. As an application area the well-known generalized traveling salesman problem (GTSP) is chosen where the set of nodes is divided into clusters so that the objective is to find a tour with minimum cost passing through exactly one node from each cluster. The experimental results show that none of the single populated variants was effective in solving all the GTSP instances whereas the eDDE performed substantially better than the single populated variants on a set of problem instances. Furthermore through the experimental analysis of results the performance of the eDDE algorithm is also compared against the best performing algorithms from the literature. Ultimately all of the best known averaged solutions for larger instances are further improved by the eDDE algorithm. © 2009 Elsevier Inc. All rights reserved. © 2009 Elsevier B.V. All rights reserved.
Citation - WoS: 103
Citation - Scopus: 129
Dynamic multi-swarm particle swarm optimizer with harmony search
(PERGAMON-ELSEVIER SCIENCE LTD, 2011) S. -Z. Zhao; P. N. Suganthan; Quan-Ke Pan; M. Fatih Tasgetiren; Suganthan, P.N.; Tasgetiren, M. Fatih; Zhao, S.-Z.; Fatih Tasgetiren, M.; Pan, Quan-Ke
In this paper the dynamic multi-swarm particle swarm optimizer (DMS-PSO) is improved by hybridizing it with the harmony search (HS) algorithm and the resulting algorithm is abbreviated as DMS-PSO-HS. We present a novel approach to merge the HS algorithm into each sub-swarm of the DMS-PSO. Combining the exploration capabilities of the DMS-PSO and the stochastic exploitation of the HS the DMS-PSO-HS is developed. The whole DMS-PSO population is divided into a large number of small and dynamic sub-swarms which are also individual HS populations. These sub-swarms are regrouped frequently and information is exchanged among the particles in the whole swarm. The DMS-PSO-HS demonstrates improved on multimodal and composition test problems when compared with the DMS-PSO and the HS. (C) 2010 Elsevier Ltd. All rights reserved.
Citation - WoS: 99
Citation - Scopus: 113
Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion
(Elsevier Ltd, 2017) M. Fatih Tasgetiren; Damla Kizilay; Quanke Pan; Ponnuthurai Nagaratnam Suganthan; Tasgetiren, M. Fatih; Kizilay, Damla; Suganthan, P.N.; Pan, Quan-Ke
Recently iterated greedy algorithms have been successfully applied to solve a variety of combinatorial optimization problems. This paper presents iterated greedy algorithms for solving the blocking flowshop scheduling problem (BFSP) with the makespan criterion. Main contributions of this paper can be summed up as follows. We propose a constructive heuristic to generate an initial solution. The constructive heuristic generates better results than those currently in the literature. We employ and adopt well-known speed-up methods from the literature for both insertion and swap neighborhood structures. In addition an iteration jumping probability is proposed to change the neighborhood structure from insertion neighborhood to swap neighborhood. Generally speaking the insertion neighborhood is much more effective than the swap neighborhood for the permutation flowshop scheduling problems. Instead of considering the use of these neighborhood structures in a framework of the variable neighborhood search algorithm two powerful local search algorithms are designed in such a way that the search process is guided by an iteration jumping probability determining which neighborhood structure will be employed. By doing so it is shown that some additional enhancements can be achieved by employing the swap neighborhood structure with a speed-up method without jeopardizing the effectiveness of the insertion neighborhood. We also show that the performance of the iterated greedy algorithm significantly depends on the speed-up method employed. The parameters of the proposed iterated greedy algorithms are tuned through a design of experiments on randomly generated benchmark instances. Extensive computational results on Taillard's well-known benchmark suite show that the iterated greedy algorithms with speed-up methods are equivalent or superior to the best performing algorithms from the literature. Ultimately 85 out of 120 problem instances are further improved with substantial margins. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 9
Citation - Scopus: 13
Solving fuzzy job-shop scheduling problem by a hybrid PSO algorithm
(Springer-Verlag Berlin, 2012) Junqing Li; Quanke Pan; Ponnuthurai Nagaratnam Suganthan; M. Fatih Tasgetiren; Li, Junqing; Suganthan, P.N.; Tasgetiren, M. Fatih; Pan, Quan-Ke
This paper proposes a hybrid particle swarm optimization (PSO) algorithm for solving the job-shop scheduling problem with fuzzy processing times. The objective is to minimize the maximum fuzzy completion time i.e. the fuzzy makespan. In the proposed PSO-based algorithm performs global explorative search while the tabu search (TS) conducts the local exploitative search. One-point crossover operator is developed for the individual to learn information from the other individuals. Experimental results on three well-known benchmarks and a randomly generated case verify the effectiveness and efficiency of the proposed algorithm. © 2012 Springer-Verlag. © 2012 Elsevier B.V. All rights reserved.