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IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS)

作者： Tasgetiren, M. Fatih Pan, Quan-Ke Suganthan, P. N. Dizbay, Ikbal Ece Yasar Univ Dept Ind Engn Selcuk Yasar Campus Izmir Turkey Northeastern Univ SKL Synthet Automat Proc Ind Shenyang 110819 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781467359054

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.

关键词： quadratic assignment problem iterated greedy algorithm differential evolution algorithm migrating birds optimization

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Metaheuristic algorithms for the Quadratic Assignment Problem

Metaheuristic Algorithms for the Quadratic Assignment Proble...

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IEEE Symposium on Computational Intelligence In Production and Logistics Systems

作者： M. Fatih Tasgetiren Quan-Ke Pan P. N. Suganthan Ikbal Ece Dizbay Industrial Engineering Department Selcuk Yasar Campus Yasar University SKL of Synthetical Automation for Process Industries (Northeastern University) School of Electrical and Electronic Engineering Nanyang Technological University

ISBN: (纸本)9781467359047

This paper presents two meta-heuristic algorithms to solve the quadratic assignment problem. The iterated greedy algorithm has two main components, which 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.

关键词： Quadratic assignment problem iterated greedy algorithm Differential evolution algorithm Migrating birds optimization

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A Discrete Artificial Bee Colony algorithm for the Traveling Salesman Problem with Time Windows

A Discrete Artificial Bee Colony Algorithm for the Traveling...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Karabulut, Korhan Tasgetiren, M. Fatih Yasar Univ Software Engn Dept Selcuk Yasar Campus Izmir Turkey Ind Engn Dept Yasar Univ Izmir Turkey

ISBN: (纸本)9781467315098

This paper presents a discrete artificial bee colony algorithm (DABC) for solving the traveling salesman problem with time windows (TSPTW) in order to minimize the total travel cost of a given tour. TSPTW is a difficult optimization problem arising in both scheduling and logistic applications. The proposed DABC algorithm basically relies on the destruction and construction phases of iterated greedy algorithm to generate neighboring food sources in a framework of ABC algorithm. In addition, it also relies on a classical 1-opt local search algorithm to further enhance the solution quality. The performance of the algorithm was tested on a benchmark set from the literature. Experimental results show that the proposed DABC algorithm is very competitive to or even better than the best performing algorithms from the literature.

关键词： traveling salesman problem with time windows artificial bee colony algorithm iterated greedy algorithm swarm intelligence heuristic optimization

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A Discrete Artificial Bee Colony algorithm for the Team Orienteering Problem with Time Windows

A Discrete Artificial Bee Colony Algorithm for the Team Orie...

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IEEE Symposium on Computational Intelligence In Production and Logistics Systems

作者： Korhan Karabulut M. Fatih Tasgetiren Software Engineering Department Yasar University Industrial Engineering Department Yasar University

ISBN: (纸本)9781467359047

This paper presents a discrete artificial bee colony algorithm (DABC) for solving the team orienteering problem with time windows (TOPTW). The proposed algorithm employs a destruction and construction procedure to generate neighboring food sources in the framework of the DABC algorithm. In addition, a variable neighborhood descent (VND) algorithm is developed to enhance the solution quality. The performance of the algorithm was tested on a benchmark set from the literature. Experimental results show that the proposed DABC algorithm is competitive to the best performing algorithms from the literature. Ultimately, 11 instances are further improved by the proposed DABC algorithm.

关键词： Team orienteering problem with time windows Artificial bee colony algorithm iterated greedy algorithm Swarm intelligence Heuristic optimization

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A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops

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INFORMATION SCIENCES 2011年第16期181卷 3459-3475页

作者： Tasgetiren, M. Fatih Pan, Quan-Ke Suganthan, P. N. Chen, Angela H-L Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Yasar Univ Dept Ind Engn Izmir Turkey Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Nanya Inst Technol Dept Finance Tao Yuan 320 Taiwan

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. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Permutation flowshop scheduling problem iterated greedy algorithm Discrete differential evolution algorithm Discrete artificial bee colony algorithm Estimation of distribution algorithm Genetic local search

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A discrete differential evolution algorithm for the single machine total weighted tardiness problem with sequence dependent setup times

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COMPUTERS & OPERATIONS RESEARCH 2009年第6期36卷 1900-1915页

作者： Tasgetiren, M. Fatih Pan, Quan-Ke Liang, Yun-Chia Sultan Qaboos Univ Dept Operat Management & Business Stat Muscat Oman Liaocheng Univ Coll Comp Sci Liaocheng 252059 Shandong Peoples R China

This paper is concerned with solving the single machine total weighted tardiness problem with sequence dependent setup times by a discrete differential evolution algorithm developed by the authors recently. Its performance is enhanced by employing different population initialization schemes based on some constructive heuristics such as the well-known NEH and the greedy randomized adaptive search procedure (GRASP) as well as some priority rules such as the earliest weighted due date (EWDD) and the apparent tardiness cost with setups (ATCS). Additional performance enhancement is further achieved by the inclusion of a referenced local search (RLS) in the algorithm together with the use of destruction and construction (DC) procedure when obtaining the mutant population. Furthermore, to facilitate the greedy job insertion into a partial solution which will be employed in the NEH. GRASP, DC heuristics as well as in the RLS local search, some newly designed speed-up methods are presented for the insertion move for the first time in the literature. They are novel contributions of this paper to the single machine tardiness related scheduling problems with sequence dependent setup times. To evaluate its performance, the discrete differential evolution algorithm is tested on a set of benchmark instances from the literature. Through the analyses of experimental results, its highly effective performance with substantial margins both in solution quality and CPU time is shown against the best performing algorithms from the literature, in particular, against the very recent newly designed particle swarm and ant colony optimization algorithms of Anghinolfi and Paolucci [A new discrete particle swarm optimization approach for the single machine total weighted tardiness scheduling problem with sequence dependent setup times. European journal of Operational Research 2007;doi:10.1016/***.2007.10.044] and Anghinolfi and Paolucci [A new ant colony optimization approach for the single m

关键词： Discrete differential evolution algorithm Single machine total weighted tardiness problem with sequence dependent setup times iterated greedy algorithm greedy randomized adaptive search procedure

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A discrete differential evolution algorithm for the permutation flowshop scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2008年第4期55卷 795-816页

作者： Pan, Quan-Ke Tasgetiren, Mehmet Fatih Liang, Yun-Chia Sultan Qaboos Univ Dept Operat Management & Business Stat Muscat Oman Liaocheng Univ Coll Comp Sci Liaocheng Peoples R China Yuan Ze Univ Dept Ind Engn & Management Chungli 320 Taiwan

Very recently, Pan et al. [Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECC007, pp. 126-33] presented a new and novel discrete differential evolution algorithm for the permutation flowshop scheduling problem with the makespan criterion. On the other hand, the iterated greedy algorithm is proposed by [Ruiz, R.. & Stutzle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-49] for the permutation flowshop scheduling problem with the makespan criterion. However, both algorithms are not applied to the permutation flowshop scheduling problem with the total flowtime criterion. Based on their excellent performance with the makespan criterion, we extend both algorithms in this paper to the total flowtime objective. Furthermore, we propose a new and novel referenced local search procedure hybridized with both algorithms to further improve the solution quality. The referenced local search exploits the space based on reference positions taken from a reference solution in the hope of finding better positions for jobs when performing insertion operation. Computational results show that both algorithms with the referenced local search are either better or highly competitive to all the existing approaches in the literature for both objectives of makespan and total flowtime. Especially for the total flowtime criterion, their performance is superior to the particle swarm optimization algorithms proposed by [Tasgetiren, M. F., Liang, Y. -C., Sevkli, M., Gencyilmaz, G. (2007). Particle swarm optimization algorithm for makespan and total flowtime minimization in permutation flowshop sequencing problem. European journal of Operational Research, 177(3), 1930-47] and [Jarboui, B., Ibrahim, S., Siarry, P., Rebai, A. (2007). A combinatorial particle swarm optimisation for solving permutation flowshop problems. Computers & Industrial Engineeri

关键词： Permutation flowshop Scheduling iterated greedy algorithm Discrete differential evolution algorithm Discrete particle swarm optimization Referenced local search

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A Discrete Particle Swarm Optimization algorithm for the Generalized Traveling Salesman Problem 07

A Discrete Particle Swarm Optimization Algorithm for the Gen...

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Tasgetiren, M. Fatih Suganthan, P. N. Pan, Quan-Ke Sultan Qaboos Univ Dept Operat Management & Business Stat Muscat Oman Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Liaocheng Univ Coll Comp Sci Liaocheng 252059 Shandong Peoples R China

ISBN: (纸本)9781595936974

Dividing the set of nodes into clusters in the well-known traveling salesman problem results in the generalized traveling salesman problem which seeking a tour with minimum cost passing through only a single node from each cluster. In this paper, a discrete particle swarm optimization is presented to solve the problem on a set of benchmark instances. The discrete particle swarm optimization algorithm exploits the basic features of its continuous counterpart. It is also hybridized with a local search, variable neighborhood descend algorithm, to further improve the solution quality. In addition, some speed-up methods for greedy node insertions are presented. The discrete particle swami optimization algorithm is tested on a set of benchmark instances with symmetric distances up to 442 nodes from the literature. Computational results show that the discrete particle optimization algorithm is very promising to solve file generalized traveling salesman problem.

关键词： Generalized traveling salesman problem discrete particle swarm optimization problem iterated greedy algorithm variable neighborhood descend algorithm

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