DOCSLIB.ORG

A Combined Simulated Annealing and Tabu Search Strategy to Solve a Network Design Problem with Two Classes of Users

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Combined Simulated Annealing and Tabu Search Strategy to Solve a Network Design Problem with Two Classes of Users Copyright Warning & Restrictions The copyright law of the United States (Title 17, United States Code) governs the making of photocopies or other reproductions of copyrighted material. Under certain conditions specified in the law, libraries and archives are authorized to furnish a photocopy or other reproduction. One of these specified conditions is that the photocopy or reproduction is not to be “used for any purpose other than private study, scholarship, or research.” If a, user makes a request for, or later uses, a photocopy or reproduction for purposes in excess of “fair use” that user may be liable for copyright infringement, This institution reserves the right to refuse to accept a copying order if, in its judgment, fulfillment of the order would involve violation of copyright law. Please Note: The author retains the copyright while the New Jersey Institute of Technology reserves the right to distribute this thesis or dissertation Printing note: If you do not wish to print this page, then select “Pages from: first page # to: last page #” on the print dialog screen The Van Houten library has removed some of the personal information and all signatures from the approval page and biographical sketches of theses and dissertations in order to protect the identity of NJIT graduates and faculty. ASAC A COMIE SIMUAE AEAIG A AU SEAC SAEGY O SOE A EWOK ESIG OEM WI WO CASSES O USES y Qieg e g A methodology to solve a transportation network design problem (TCNDP) with two classes of users (passenger cars and trucks) is developed. Given an existing highway system, with a capital investment budget constraint, the methodology selects the best links to be expanded by an extra lane by considering one of three types of traffic operations: exclusive for passenger cars, exclusive for trucks, and for both passenger cars and trucks such that the network total user equilibrium (UE) travel time is minimized. The problem is formulated as an NP-hard combinatorial nonlinear integer programming problem. The classical branch and bound methodology for the integer programming problem is very inefficient in solving this computationally hard problem. A combined simulated annealing and tabu search strategy (SA-TABU), was developed which is shown to perform in a robust and efficient manner in solving five networks ranging from 36 to 332 links. A comprehensive heuristic evaluation function (HEF), a core for the heuristic search strategy, was developed which can be adjusted to the characteristics of the problem and the search strategy used. It is composed of three elements: the link volume to capacity ratio, the historical contribution of the link to the objective function, and a random variable which resembles the error term of the HEF. The principal characteristics of the SA-TABU are the following: HEF, Markov chain length, "temperature" dropping rate and the tabu list length. Sensitivity analysis was conducted in identifying the best parameter values of the main components of the SA-TABU. Sufficiently "good" solutions were found in all the problems within a rather short computational time. The solution results suggest that in most of the scenarios, the shared lane option, passenger cars and trucks, was found to be the most favored selection. Expanding approximately 10% of the links, results in a very high percentage improvement ranging from 73% to 97% for the five test networks. A COMIE SIMUAE AEAIG A AU SEAC SAEGY O SOE A EWOK ESIG OEM WI WO CASSES O USES y Qieg eg A isseaio Sumie o e acuy o ew esey Isiue o ecoogy i aia uime o e equiemes o e egee o oco o iosoy i asoaio Ieisciiay ogam i asoaio May 199 Copyright © 1997 by Qifeng Zeng ALL RIGHTS RESERVED APPROVAL PAGE A COMBINED SIMULATED ANNEALING AND TABU SEARCH STRATEGY TO SOLVE A NETWORK DESIGN PROBLEM WITH TWO CLASSES OF USERS Qifeng Zeng Dr. Kyriacos C. Mouskos, Dissertation Adviser Date Assistant Professor of Civil and Environmental Engineering, NJIT Dr. Athanassios K. Bladikas, Committee Member Date Associate Professor of Industrial and Manufacturing Engineering, NJIT Di. Norbert Op nheim, Committee Member fj‘te Pkofessor of Civil Engineering_ City University of New York Dr. Louis J. FignatarolCommittee Member Date Executive Director of Institute for Transportation, NJIT Dr. Lazar N. Spasovic, Committee Member Date Associate Professor of Management, NJIT BIOGRAPHICAL SKETCH Author: Qifeng Zeng Degree: Doctor of Philosophy in Transportation Date: May 1998 Undergraduate and Graduate Education: • Doctor of Philosophy in Transportation, New Jersey Institute of Technology, Newark, NJ, 1998 • Master of Science in Transportation, The City College of New York, New York, NY, 1992 • Bachelor of Science in Civil Engineering, Southeast University, NanJing, P.R.China, 1988 Major: Transportation Presentations and Publications: Q. Zeng and K. C. Mouskos, "Simulated Annealing Heuristic Search Strategy to Solve a Two Classes of Users Transportation Network Design Problem," Presented at INFORMS Conference, Dallas, Texas, Fall 1997. K. C. Mouskos and Q. Zeng, "A Simulated Annealing Heuristic Search Strategy for the User Equilibrium Transportation Network Design Problem," Present at INFORMS Conference, Washington, D. C., Spring 1996. This thesis is dedicated to My Parent, Zeng, Xiang Jian and Zheng, Shu Xiu, and my wife, Chan, Mei Mei ACKNOWLEDGMENT I would like to express my sincere appreciation to my supervisor Dr. Kyriacos C. Mouskos for his continuous support, guidance, and friendship throughout my Ph.D. study and research work. Special thanks to Dr. Louis J. Pignataro, for his supervision and recommendation for the funding of this study, and Dr. Norbert Oppenheim, whose encouragement and recommendation brought me to NJIT Ph.D. program. I would also like to extend my appreciation to Dr. Athanassios Bladikas and Dr. Lazar N. Spasovic for their advice and supports. Finally, I would like to extend my gratitude to my classmates in NJIT transportation program. vi TABLE OF CONTENTS Chapter Page 1. INTRODUCTION 1 1.1 Overview of Current Urban Transportation System and Network Design Problem. 1 1.2 Overview of the Problem Structure and Methodology 4 1.3 Motivation and Objectives 6 1.4 Overview of this Dissertation 9 2. LITERATURE REVIEW 11 2.1 Transportation Network Design 11 2.1.1 Bilevel Programming 13 2.1.2 Branch and Bound Algorithm 16 2.1.3 Heuristics Search Strategies and Network Design 18 2.2 Traffic Assignment Models and Formulations 21 2.2.1 Wardrop (1952) Principles 21 2.2.2 Variational Inequality Theory 22 2.2.3 Single Class User Equilibrium Assignment Model 23 2.2.4 Two Classes User Equilibrium Assignment Mathematical Formulation 25 2.3 Heuristic Search Strategies 28 2.3.1 Simulated Annealing 28 2.3.2 Tabu Search Strategy 34 vii TABLE OF CONTENTS (Continued) Chapter Page 3. FORMULATION OF TWO CLASSES OF USERS EQUILIBRIUM TRANSPORTATION NETWORK DESIGN PROBLEM (TCNDP) ADDRESSED IN THIS STUDY 39 4. CHARACTERISTICS OF THE TRAFFIC ASSIGNMENT PROCEDURE USED IN THE TCNDP 44 4.1 Review of Traffic Assignment Algorithm 44 4.1.1 Frank-Wolfe Algorithm (Convex Combination Method) 44 4.1.2 Diagonalization Algorithm for Two Classes of Users Traffic Assignment 46 4.2 Travel Cost Functions 48 4.2.1 Link Travel Cost Function 48 4.2.1.1 BPR Single Class Travel Cost Function 48 4.2.1.2 Two Classes of Users Link Travel Cost Functions 49 4.2.2 Link Travel Cost Functions (LTCF) Used in this Study 50 4.3 Numerical Experiment of Diagonalization Algorithm 61 4.4 Numerical Experiment of Diagonalization Algorithm on Five Test Networks 69 4.4.1 Five Test Networks 70 4.4.2 Test of Convergence and Convergence Rate with Number of Internal Iteration 76 4.4.3 Test of Network Flow Equilibrium and Distribution Pattern 83 viii TABLE OF CONTENTS (Continued) Chapter Page 5. DEVELOPMENT OF THE COMBINED SIMULATED ANNEALING AND TABU SEARCH STRATEGY (SA-TABU) 94 5.1 General Background of the Combined Simulated Annealing and Tabu Search Algorithm in This Study 94 5.2 Heuristics Based Combined Simulated Annealing and Tabu Search 96 5.2.1 Type of Moves 97 5.2.2 Heuristic Evaluation Function (HEF) 100 5.2.3 Search Strategy 103 5.3 General Procedure of the Heuristic Based Simulated Annealing and Tabu Search Strategy (SA-TABU). 104 5.4 Implementation of the SA-TABU Search Strategy 113 5.4.1 Main Program (Figure 5.4.1-5 4 2) 113 5.4.2 Subroutines 122 6. NUMERICAL EXPERIMENTS OF THE SA-TABU SEARCH STRATEGY 136 6.1 Overview of the Numerical Experiment 136 6.2 Test Networks 139 6.3 Numerical Results of the SA-TABU Search Strategy in Solving the SCNDP 141 6.4 Numerical Experiments of the SA-TABU Search Strategy in Solving the TCNDP Problem 154 6.5 Sensitivity Analysis of the Key Components of the SA-TABU Search Strategy. 167 ix TABLE OF CONTENTS (Continued) Chapter Page 6.5.1 Sensitivity Study on Markov Chain Length and Control Parameter 168 6.5.2 Sensitivity Analysis on Tabu List Length 179 6.5.3 Sensitivity Analysis on Heuristic Evaluation Functions (HEF) 182 7. TWO CLASSES OF USERS NETWORK DESIGN PROBLEM STUDY 185 7.1 Study of the Characteristics of the TCNDP 185 7.2 The Experimental Results of the Network Design Solutions 194 8. CONCLUSION AND FUTURE RESEARCH 205 8.1 Summary 205 8.2 Conclusions 209 8.2.1 TCNDP Characteristics 209 8.2.2 Characteristics of the SA-TABU Search Strategy and the Traffic Assignment 211 8.3 Future Research 213 REFERENCES 216 LIST OF TABLES Table Page 4.2.1 Volume/Delay Functions (Florian et.al. 1976) 51 4.3.1 Iteration of the Diagonalization Procedure for the Example 64 4.3.2 Iterations of the Streamlined Algorithm Procedure
Load more

Recommended publications
  • An Improved Tabu Search Algorithm for the Fixed-Spectrum Frequency-Assignment Problem Roberto Montemanni, Jim N
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 52, NO. 3, MAY 2003 891 An Improved Tabu Search Algorithm for the Fixed-Spectrum Frequency-Assignment Problem Roberto Montemanni, Jim N. J. Moon, and Derek H. Smith Abstract—A tabu search algorithm with a dynamic tabu list for this meant that several or many pairs of transmitters had the fixed-spectrum frequency-assignment problem is presented. inadequate frequency separation. It is now recognized that For cellular problems, the algorithm can be combined with an the pattern of interference obtained when this approach is efficient cell reoptimization step. The algorithm is tested on several sets of test problems and compared with existing algorithms of used may be quite unsatisfactory. One way to overcome the established performance. In particular, it is used to improve problem is to minimize a measure of interference based on some of the best existing assignments for COST 259 benchmarks. the signal-to-interference ratio (SIR) at specified reception These results add support to the claim that the algorithm is the points [10], [11]. This approach is conceptually attractive as most effective available, at least when solution quality is a more interference is directly minimized and it can take account of important criterion than solution speed. The algorithm is robust and easy to tune. multiple interference. However, it is computationally demanding and may restrict the size of the problems that can be handled. Index Terms—Fixed-spectrum problems, radio-frequency Our recent experiences with this approach suggest that, if data assignment, tabu search algorithm. structures of similar efficiency are used, run times are increased by a factor of about 20.
    [Show full text]
  • A Tabu Search Algorithm with Efficient Diversification Strategy for High School Timetabling Problem
    International Journal of Computer Science & Information Technology (IJCSIT) Vol 5, No 4, August 2013 A TABU SEARCH ALGORITHM WITH EFFICIENT DIVERSIFICATION STRATEGY FOR HIGH SCHOOL TIMETABLING PROBLEM Salman Hooshmand1 , Mehdi Behshameh2 and OmidHamidi3 1Department of Computer Engineering, Hamedan University of Technology, Hamedan Iran [email protected] 2Department of Computer Engineering, Islamic Azad University ,Toyserkan Branch, Toyserkan, Iran [email protected] 3Department of Science, Hamedan University of Technology, Hamedan, Iran [email protected] ABSTRACT The school timetabling problem can be described as scheduling a set of lessons (combination of classes, teachers, subjects and rooms) in a weekly timetable. This paper presents a novel way to generate timetables for high schools. The algorithm has three phases. Pre-scheduling, initial phase and optimization through tabu search. In the first phase, a graph based algorithm used to create groups of lessons to be scheduled simultaneously; then an initial solution is built by a sequential greedy heuristic. Finally, the solution is optimized using tabu search algorithm based on frequency based diversification. The algorithm has been tested on a set of real problems gathered from Iranian high schools. Experiments show that the proposed algorithm can effectively build acceptable timetables. KEYWORDS Timetabling. Tabu Search. Diversification .Graph . Scheduling 1. INTRODUCTION Scheduling high school lessons has been considered to be one of the main concerns of schools’ staff before beginning of the term. Manual timetabling is tedious and generally takes several weeks to build an acceptable timetable. The main difficulty is related to the size of the problem; the algorithm should consider several conflicting criteria and conditions often for a large number of classes, teachers and courses.
    [Show full text]
  • Solving Zero-One Mixed Integer Programming Problems Using Tabu Search
    Solving Zero-One Mixed Integer Programming Problems Using Tabu Search by Arne Løkketangen * Fred Glover # 20 April 1997 Abstract We describe a tabu search approach for solving general zero- one mixed integer programming problems that exploits the extreme point property of zero-one solutions. Specialized choice rules and aspiration criteria are identified for the problems, expressed as functions of integer infeasibility measures and objective function values. The first-level TS mechanisms are then extended with advanced level strategies and learning. We also look at probabilistic measures in this framework, and examine how the learning tool Target Analysis can be applied to identify better control structures and decision rules. Computational results are reported on a portfolio of multiconstraint knapsack problems. Our approach is designed to solve thoroughly general 0/1 MIP problems and thus contains no problem domain specific knowledge, yet it obtains solutions for the multiconstraint knapsack problem whose quality rivals, and in some cases surpasses, the best solutions obtained by special purpose methods that have been created to exploit the special structure of these problems. Keywords Tabu Search, Heuristics, Integer Programming #* School of Business, CB419 Molde College University of Colorado Britvn. 2 Boulder, CO 80309 6400 Molde USA Norway Email: [email protected] Email: [email protected] 2 Introduction Tabu search (TS) has been applied to solving a variety of zero-one mixed integer programming (MIP) problems with special structures, ranging from scheduling and routing to group technology and probabilistic logic. (See, for example, the volume Annals of Operations Research 41 (1993), and the application surveys in Glover and Laguna (1993), and Glover (1996)).
    [Show full text]
  • Dynamic Temporary Blood Facility Locationallocation During And
    Dynamic temporary blood facility location-allocation during and post-disaster periods Article (Accepted Version) Sharma, Bhuvnesh, Ramkumar, M, Subramanian, Nachiappan and Malhotra, Bharat (2017) Dynamic temporary blood facility location-allocation during and post-disaster periods. Annals of Operations Research. ISSN 0254-5330 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/id/eprint/71673/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher’s version. Please see the URL above for details on accessing the published version. Copyright and reuse: Sussex Research Online is a digital repository of the research output of the University. Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available. Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. http://sro.sussex.ac.uk Dynamic temporary blood facility location-allocation during and post-disaster periods Bhuvnesh Sharma Department of Industrial & Systems Engineering Indian Institute of Technology Kharagpur Kharagpur – 721302, West Bengal, India e-mail: [email protected] M.
    [Show full text]
  • A Comparative Analysis of a Tabu Search and a Genetic Algorithm for Solving a University Course Timetabling Problem
    DEGREE PROJECT, IN COMPUTER SCIENCE , FIRST LEVEL STOCKHOLM, SWEDEN 2015 A comparative analysis of a Tabu Search and a Genetic Algorithm for solving a University Course Timetabling Problem CASPER RENMAN AND HAMPUS FRISTEDT KTH ROYAL INSTITUTE OF TECHNOLOGY CSC SCHOOL www.kth.se iii Abstract This work implements a Tabu search (TS) algorithm for solving an instance of the University Course Timetabling Problem (UCTP). The algorithm is com- pared to a Genetic algorithm (GA) implemented by Yamazaki and Pertoft (2014) that solves the same instance. The purpose of the work is to explore how the approaches differ for this particular instance, and specifically inves- tigate whether a TS algorithm alone is sufficient, considering the small size of the UCTP instance. The TS was based on the OpenTS library (Harder, 2001) and reuses parts from Yamazaki and Pertoft’s (2014) GA. The results show that the TS alone is too slow. Even in a small instance of the UCTP, the search space is too big for the TS to be fast. This confirms the state of the art, that a combination of TS and GA would perform better than either of them individually. Further improvements on the TS would involve reducing the number of moves generated each iteration. iv Sammanfattning Detta arbete implementerar en Tabu search-algoritm (TS) för att lösa en in- stans av University Course Timetabling Problem (UCTP). Algoritmen jämförs med en Genetic algoritm (GA) implementerad av Yamazaki och Pertoft (2014) som löser samma instans. Syftet med arbetet är att utforska hur angreppssätten skiljer sig för denna instans, och specifikt undersöka om det räcker att endast använda en TS-algoritm, eftersom instansen är liten.
    [Show full text]
  • Exploiting the Power of Local Search in a Branch and Bound Algorithm for Job Shop Scheduling Matthew J
    Exploiting the Power of Local Search in a Branch and Bound Algorithm for Job Shop Scheduling Matthew J. Streeter1 and Stephen F. Smith2 Computer Science Department and Center for the Neural Basis of Cognition1 and The Robotics Institute2 Carnegie Mellon University Pittsburgh, PA 15213 {matts, sfs}@cs.cmu.edu Abstract nodes in the branch and bound search tree. This paper presents three techniques for using an iter- 2. Branch ordering. In practice, a near-optimal solution to ated local search algorithm to improve the performance an optimization problem will often have many attributes of a state-of-the-art branch and bound algorithm for job (i.e., assignments of values to variables) in common with shop scheduling. We use iterated local search to obtain an optimal solution. In this case, the heuristic ordering (i) sharpened upper bounds, (ii) an improved branch- of branches can be improved by giving preference to a ordering heuristic, and (iii) and improved variable- branch that is consistent with the near-optimal solution. selection heuristic. On randomly-generated instances, our hybrid of iterated local search and branch and bound 3. Variable selection. The heuristic solutions used for vari- outperforms either algorithm in isolation by more than able selection at each node of the search tree can be im- an order of magnitude, where performance is measured proved by local search. by the median amount of time required to find a globally optimal schedule. We also demonstrate performance We demonstrate the power of these techniques on the job gains on benchmark instances from the OR library. shop scheduling problem, by hybridizing the I-JAR iter- ated local search algorithm of Watson el al.
    [Show full text]
  • Chapter 4 TABU SEARCH
    Chapter 4 TABU SEARCH Fred Glover1 and Rafael Martí2 1 Leeds School of Business, University of Colorado, Campus Box 419, Boulder, CO 80309; 2 Dpto. de Estadística e Investigación Operativa, Universidad de Valencia, Dr. Moliner 50, 46100 Burjassot (Valencia) Spain Abstract: Tabu Search is a meta-heuristic that guides a local heuristic search procedure to explore the solution space beyond local optimality. One of the main components of Tabu Search is its use of adaptive memory, which creates a more flexible search behavior. Memory-based strategies are therefore the hallmark of tabu search approaches, founded on a quest for “integrating principles,” by which alternative forms of memory are appropriately combined with effective strategies for exploiting them. In this chapter we address the problem of training multilayer feed-forward neural networks. These networks have been widely used for both prediction and classification in many different areas. Although the most popular method for training these networks is back propagation, other optimization methods such as tabu search have been applied to solve this problem. This chapter describes two training algorithms based on the tabu search. The experimentation shows that the procedures provide high quality solutions to the training problem, and in addition consume a reasonable computational effort. Key words: Intelligent Problem Solving, Memory Structures, Adaptive Memory Programming. 1. INTRODUCTION The basic form of Tabu Search (TS) is founded on ideas proposed by Fred Glover (1977, 1986). The method is based on procedures designed to cross boundaries of feasibility or local optimality, instead of treating them as barriers. Early examples of these procedures, derived from surrogate 2 Chapter 4 constraint methods and cutting plane approaches, systematically impose and released constraints to permit exploration of otherwise forbidden regions.
    [Show full text]
  • Tabu Search for Constraint Satisfaction
    Tabu Search for Constraint Solving and Its Applications Jin-Kao Hao LERIA University of Angers 2 Boulevard Lavoisier 49045 Angers Cedex 01 - France 1. Introduction The Constraint Satisfaction Problem (CSP) is a very general problem able to conveniently formulate a wide range of important applications in combinatorial optimization. These include well-known classical problems such as graph k-coloring and satisfiability, as well as many practical applications related to resource assignments, planning and timetabling. The feasibility problem of Integer Programming can also be easily represented by the CSP formalism. As elaborated below, we provide case studies for specific CSP applications, including the important realm of frequency assignment, and establish that tabu search is the best performing approach for these applications. The Constraint Satisfaction Problem is defined by a triplet (X, D, C) where: – X = {x1,· · ·,xn} is a finite set of n variables. – D = {Dx1 ,· · ·,Dxn} is a set of associated domains. Each domain Dxi specifies the finite set of possible values of the variable xi. – C = {C1,· · ·,Cp} is a finite set of p constraints. Each constraint is defined on a set of variables and specifies which combinations of values are compatible for these variables. Given such a triplet, the basic problem consists in finding a complete assignment of the values to the variables such that that all the constraints are satisfied. Such an assignment is called a solution to the given CSP instance. Since the set of all assignments is given by the Cartesian product Dx1 ×· · ·×Dxn, solving a CSP means to determine a particular assignment among a potentially huge search space.
    [Show full text]
  • Tabu Search Tabu Search Is a Mathematical Optimization Method, Belonging to the Class of Trajectory Based Techniques
    CSE4403 3.0 & CSE6002E - Soft Computing Winter Semester, 2011 ________________________________________________________________________________________________________ Tabu search Tabu search is a mathematical optimization method, belonging to the class of trajectory based techniques. Tabu search enhances the performance of a local search method by using memory structures that describe the visited solutions: once a potential solution has been determined, it is marked as "taboo" ("tabu" being a different spelling of the same word) so that the algorithm does not visit that possibility repeatedly. Tabu search is attributed to Fred W. Glover. Basic details Tabu search is a metaheuristic algorithm that can be used for solving combinatorial optimization problems, such as the traveling salesman problem (TSP). Tabu search uses a local or neighborhood search procedure to iteratively move from a solution x to a solution x' in the neighborhood of x , until some stopping criterion has been satisfied. To explore regions of the search space that would be left unexplored by the local search procedure (see local optimality), tabu search modifies the neighborhood structure of each solution as the search progresses. The solutions admitted to N * (x) , the new neighborhood, are determined through the use of memory structures. The search then progresses by iteratively moving from a solution x to a solution x' in N * (x) . Perhaps the most important type of memory structure used to determine the solutions admitted to N * (x) is the tabu list. In its simplest form, a tabu list is a short-term memory, which contains the solutions that have been visited in the recent past (less than n iterations ago, where n is the number of previous solutions to be stored ( n is also called the tabu tenure)).
    [Show full text]
  • Discrete Tabu Search for Graph Matching
    Discrete Tabu Search for Graph Matching Kamil Adamczewski Yumin Suh [email protected] [email protected] Kyoung Mu Lee [email protected] Department of ECE, ASRI, Seoul National University Abstract forward to combine into the IQP optimization process. As a result, various methods have been developed to approx- Graph matching is a fundamental problem in computer imate the optimal solution of the graph matching problem vision. In this paper, we propose a novel graph match- based on different approaches, including eigenvector analy- ing algorithm based on tabu search [13]. The proposed sis [27, 20], random walk [7], stochastic methods [26] and method solves graph matching problem by casting it into other optimization techniques [21, 32]. an equivalent weighted maximum clique problem of the corresponding association graph, which we further penal- Conventionally, two stage procedure has been used to ize through introducing negative weights. Subsequent tabu approximate the solution of IQP: first, solve the optimiza- search optimization allows for overcoming the convention tion problem in continuous domain by relaxing integer con- of using positive weights. The method’s distinct feature is straint, and then project the intermediate solution into the that it utilizes the history of search to make more strate- original feasible space of IQP [20, 7, 14]. Nonetheless, re- gic decisions while looking for the optimal solution, thus cent research [21, 30] devises methods that produce discrete effectively escaping local optima and in practice achiev- solutions and suggests that post-processing discretization ing superior results. The proposed method, unlike the ex- leads to substantial loss in accuracy, especially that the dis- isting algorithms, enables direct optimization in the orig- cretization step is independent of the optimization of IQP.
    [Show full text]
  • Portfolio Optimization Models and a Tabu Search Algorithm for the Kuwait Stock Exchange”
    “Portfolio optimization models and a tabu search algorithm for the Kuwait Stock Exchange” AUTHORS Majid M. Aldaihani Talla M. Aldeehani Majid M. Aldaihani and Talla M. Aldeehani (2008). Portfolio optimization models ARTICLE INFO and a tabu search algorithm for the Kuwait Stock Exchange. Investment Management and Financial Innovations, 5(2) RELEASED ON Thursday, 26 June 2008 JOURNAL "Investment Management and Financial Innovations" FOUNDER LLC “Consulting Publishing Company “Business Perspectives” NUMBER OF REFERENCES NUMBER OF FIGURES NUMBER OF TABLES 0 0 0 © The author(s) 2021. This publication is an open access article. businessperspectives.org Investment Management and Financial Innovations, Volume 5, Issue 2, 2008 Majid M. Aldaihani (Kuwait), Talla M. Aldeehani (Kuwait) Portfolio optimization models and a tabu search algorithm for the Kuwait Stock Exchange Abstract Two mathematical models are developed to study and compare results of two cases of the portfolio selection problem in the Kuwait Stock Exchange (KSE) as an emerging market. The mathematical models are attempting to balance the trade off between risk and return by maximizing the expected return while maintaining the risk measures to certain limits. Model A measures the expected return by the moving average forecasting technique, while Model B measures the expected return by the random walk forecasting technique. Both models, which turned to be non-linear, are solved using a tailored tabu search heuristic algorithm to provide efficient solutions with reasonable amount of computational times. After testing the models using real data from KSE, the results indicated that Model A is able to beat the market in a significant manner. Keywords: portfolio selection, optimization, mathematical modeling, tabu search.
    [Show full text]
  • Tabu Search - Wikipedia, the Free Ency Clopedia Tabu Search from Wikipedia, the Free Encyclopedia
    11/15/12 Tabu search - Wikipedia, the free ency clopedia Tabu search From Wikipedia, the free encyclopedia Tabu search, created by Fred W. Glover in 1986[1] and formalized in 1989,[2][3] is a local search method used for mathematical optimization. Local searches take a potential solution to a problem and check its immediate neighbors (that is, solutions that are similar except for one or two minor details) in the hope of finding an improved solution. Local search methods have a tendency to become stuck in suboptimal regions or on plateaus where many solutions are equally fit. Tabu search enhances the performance of these techniques by using memory structures that describe the visited solutions or user-provided sets of rules.[2] If a potential solution has been previously visited within a certain short- term period or if it has violated a rule, it is marked as "taboo" ("tabu" being a different spelling of the same word) so that the algorithm does not consider that possibility repeatedly. Contents 1 Basic Description 2 Pseudo-code 3 Example: Traveling salesman problem 4 TS-PSO Algorithm 5 References 6 External links Basic Description Tabu search is a metaheuristic local search algorithm that can be used for solving combinatorial optimization problems (problems where an optimal ordering and selection of options is desired - an example, the traveling salesman problem, is detailed below). Tabu search uses a local or neighborhood search procedure to iteratively move from one potential solution to an improved solution in the neighborhood of , until some stopping criterion has been satisfied (generally, an attempt limit or a score threshold).
    [Show full text]