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Journal ArticleDOI

A Tabu Search Based Approach for Solving a Class of Bilevel Programming Problems in Chemical Engineering

01 Sep 2003-Journal of Heuristics (Kluwer Academic Publishers)-Vol. 9, Iss: 4, pp 307-319
TL;DR: The algorithm has been tested for a number of benchmark problems and the results obtained show superiority of the approach over the conventional methods in solving the bilevel programming problems.
Abstract: In this paper an approach based on the tabu search paradigm to tackle the bilevel programming problems is presented. The algorithm has been tested for a number of benchmark problems and the results obtained show superiority of the approach over the conventional methods in solving such problems.
Citations
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Journal ArticleDOI
TL;DR: A genetic algorithm for the linear bilevel problem in which both objective functions are linear and the common constraint region is a polyhedron is developed, which aims to combine classical extreme point enumeration techniques with genetic search methods by associating chromosomes with extreme points of thepolyhedron.

152 citations


Cites methods from "A Tabu Search Based Approach for So..."

  • ...Hence metaheuristic approaches have been applied for solving the LBP problem, like genetic algorithms, which are the topic of this paper, simulated annealing [1,18] or tabu search [8,16,19]....

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Journal ArticleDOI
TL;DR: An ant colony optimization based approach is developed to solve the bilevel model of a hierarchical production-distribution planning problem and uses ants to compute the routes of a feasible solution of the associated multi-depot vehicle route problem.

143 citations


Cites methods from "A Tabu Search Based Approach for So..."

  • ...Genetic algorithms have been developed in [6,20,22,24], simulated annealing is applied in [2,28] and tabu search is proposed in [17,26,32]....

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Journal ArticleDOI
TL;DR: This paper attempts to develop an efficient method based on particle swarm optimization (PSO) algorithm with swarm intelligence by comparing the results with genetic algorithm (GA) using four problems in the literature and an example of supply chain model.
Abstract: Bi-level linear programming is a technique for modeling decentralized decision. It consists of the upper-level and lower-level objectives. This paper attempts to develop an efficient method based on particle swarm optimization (PSO) algorithm with swarm intelligence. The performance of the proposed method is ascertained by comparing the results with genetic algorithm (GA) using four problems in the literature and an example of supply chain model. The results illustrate that the PSO algorithm outperforms GA in accuracy.

119 citations

Journal ArticleDOI
TL;DR: A hybrid intelligent algorithm by combining the particle swarm optimization (PSO) with chaos searching technique (CST) is presented for solving nonlinear bilevel programming problems.
Abstract: In this paper, a hybrid intelligent algorithm by combining the particle swarm optimization (PSO) with chaos searching technique (CST) is presented for solving nonlinear bilevel programming problems. The bilevel programming is transformed into a single level programming problem by use of the KKT conditions of the lower level problem. Then, the hybrid intelligent algorithm is proposed to solve the transformed problem. Our approach embeds the CST into PSO. Firstly, the algorithm is initialized by a set of random particles which travel through the search space. Secondly, an optimization problem is solved by CST to judge whether the particle is feasible or not. In each iteration, all the feasible particles are ranked in ascending order. Particles in the front of list are updated by PSO, while particles in the end of list are updated by CST. The CST used here is not only to enhance the particles but also to improve the diversity of the particle swarm so as to avoid PSO trapping the local optima. Finally, the hybrid intelligent algorithm is commented by illustrating the numerical results on several benchmark problems from the references.

106 citations

Proceedings ArticleDOI
20 Jun 2013
TL;DR: An algorithm is proposed which uses differential evolution to solve both the upper and lower level optimization problems, characterized by two optimization problems which are hierarchically related.
Abstract: Bilevel programming problems are characterized by two optimization problems which are hierarchically related, where to each feasible upper level solution an optimal solution in the lower level problem must be associated. These problems appear in many practical applications, and a variety of solution techniques can be found in the literature. In this paper, an algorithm is proposed which uses differential evolution to solve both the upper and lower level optimization problems. Several test problems from the literature are solved in order to assess the performance of the proposed method.

74 citations


Additional excerpts

  • ...445 [26,36,37] (1, 0) 17 1 BlDE (1, 0) 17 1 7 [42] (0, 2, ?, ?) -12....

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  • ...Ref (x∗, y∗) f∗ 1 f∗ 2 1 [10,31,37] (10, 10) 100 0 [24,26,36] (10, 10) 100 0 [23] (10....

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  • ...754 12 [24,43] (4, 4) -12 4 BlDE (4, 4) -12 4 13) [24,25,36] (0....

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  • ...657 1 16 [36] (1, 3) 5 4 [26] (4, 3) 2 4 BlDE (1, 3) 5 4 17 [26,36] (3, 5) 9 0 BlDE (3, 5) 9 0 18 [36] (17....

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References
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Book
31 Jul 1997
TL;DR: This book explores the meta-heuristics approach called tabu search, which is dramatically changing the authors' ability to solve a host of problems that stretch over the realms of resource planning, telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics, pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservation and scores of other problems.
Abstract: From the Publisher: This book explores the meta-heuristics approach called tabu search, which is dramatically changing our ability to solve a hostof problems that stretch over the realms of resource planning,telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics,pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservationand scores of other problems. The major ideas of tabu search arepresented with examples that show their relevance to multipleapplications. Numerous illustrations and diagrams are used to clarifyprinciples that deserve emphasis, and that have not always been wellunderstood or applied. The book's goal is to provide ''hands-on' knowledge and insight alike, rather than to focus exclusively eitheron computational recipes or on abstract themes. This book is designedto be useful and accessible to researchers and practitioners inmanagement science, industrial engineering, economics, and computerscience. It can appropriately be used as a textbook in a masterscourse or in a doctoral seminar. Because of its emphasis on presentingideas through illustrations and diagrams, and on identifyingassociated practical applications, it can also be used as asupplementary text in upper division undergraduate courses. Finally, there are many more applications of tabu search than canpossibly be covered in a single book, and new ones are emerging everyday. The book's goal is to provide a grounding in the essential ideasof tabu search that will allow readers to create successfulapplications of their own. Along with the essentialideas,understanding of advanced issues is provided, enabling researchers togo beyond today's developments and create the methods of tomorrow.

6,373 citations

Journal ArticleDOI
TL;DR: The elements of staged search and structured move sets are characterized, which bear on the issue of finiteness, and new dynamic strategies for managing tabu lists are introduced, allowing fuller exploitation of underlying evaluation functions.
Abstract: This is the second half of a two part series devoted to the tabu search metastrategy for optimization problems. Part I introduced the fundamental ideas of tabu search as an approach for guiding other heuristics to overcome the limitations of local optimality, both in a deterministic and a probabilistic framework. Part I also reported successful applications from a wide range of settings, in which tabu search frequently made it possible to obtain higher quality solutions than previously obtained with competing strategies, generally with less computational effort. Part II, in this issue, examines refinements and more advanced aspects of tabu search. Following a brief review of notation, Part II introduces new dynamic strategies for managing tabu lists, allowing fuller exploitation of underlying evaluation functions. In turn, the elements of staged search and structured move sets are characterized, which bear on the issue of finiteness. Three ways of applying tabu search to the solution of integer programmin...

5,883 citations


"A Tabu Search Based Approach for So..." refers background in this paper

  • ...Proposed by Glover (1986, 1989, 1990) , the tabu search is an intelligent metaheuristic search procedure that has found very wide applications in a variety of diverse fields....

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  • ...In machine scheduling problems, a frequency-based memory of all the previously-generated solutions has proved to be a very successful diversification strategy ( Glover, 1989, 1990 )....

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Journal ArticleDOI
TL;DR: Four key areas of Integer programming are examined from a framework that links the perspectives of artificial intelligence and operations research, and each has characteristics that appear usefully relevant to developments on the horizon.

3,985 citations


"A Tabu Search Based Approach for So..." refers background in this paper

  • ...Proposed by Glover (1986, 1989, 1990) , the tabu search is an intelligent metaheuristic search procedure that has found very wide applications in a variety of diverse fields....

    [...]

Book
01 Mar 1997
TL;DR: This chapter discusses the development of Optimization Theory and Methods for Process Design, and some of the techniques used in this work were new to the literature.
Abstract: 1. Introduction to Process Design. I. PRELIMINARY ANALYSIS AND EVALUATION OF PROCESSES. 2. Overview of Flowsheet Synthesis. 3. Mass and Energy Balances. 4. Equipment Sizing and Costing. 5. Economic Evaluation. 6. Design and Scheduling of Batch Processes. II. ANALYSIS WITH RIGOROUS PROCESS MODELS. 7. Unit Equation Models. 8. General Concepts of Simulation for Process Design. 9. Process Flowsheet Optimization. III. BASIC CONCEPTS IN PROCESS SYNTHESIS. 10. Heat and Power Integration. 11. Ideal Distillation Systems. 12. Heat Integrated Distillation Processes. 13. Geometric Techniques for the Synthesis of Reactor Networks. 14. Separating Azeotropic Mixtures. IV. OPTIMIZATION APPROACHES TO PROCESS SYNTHESIS AND DESIGN. 15. Basic Concepts for Algorithmic Methods. 16. Synthesis of Heat Exchanger Networks. 17. Synthesis of Distillation Sequences. 18. Simultaneous Optimization and Heat Integration. 19. Optimization Techniques for Reactor Network Synthesis. 20. Structural Optimization of Process Flowsheets. 21. Process Flexibility. 22. Optimal Design and Scheduling for Multiproduct Batch Plants. References. Exercises. Appendix A.: Summary of Optimization Theory and Methods. Appendix B.: Smooth Approximations for max { 0, f(x)}. Appendix C.: Computer Tools for Preliminary Process Design. Author Index. Subject Index.

1,105 citations


"A Tabu Search Based Approach for So..." refers background in this paper

  • ...Bialas and Karwan, 1984; Brengel and Seider, 1992; Biegler et al., 1997)....

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Journal ArticleDOI
TL;DR: This presentation demonstrates that a well-tuned implementation of tabu search makes it possible to obtain solutions of high quality for difficult problems, yielding outcomes in some settings that have not been matched by other known techniques.
Abstract: We describe the main features of tabu search, emphasizing a perspective for guiding a user to understand basic implementation principles for solving combinatorial or nonlinear problems. We also identify recent developments and extensions that have contributed to increasing the efficiency of the method. One of the useful aspects of tabu search is the ability to adapt a rudimentary prototype implementation to encompass additional model elements, such as new types of constraints and objective functions. Similarly, the method itself can be evolved to varying levels of sophistication. We provide several examples of discrete optimization problems to illustrate the strategic concerns of tabu search, and to show how they may be exploited in various contexts. Our presentation is motivated by the emergence of an extensive literature of computational results, which demonstrates that a well-tuned implementation makes it possible to obtain solutions of high quality for difficult problems, yielding outcomes in some settings that have not been matched by other known techniques.

941 citations