Showing papers on "Metaheuristic published in 1996"
01 Feb 1996
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Abstract: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
11,224 citations
Book•
01 Jan 1996
TL;DR: This review discusses mathematics, linear programming, and set--Constrained and Unconstrained Optimization, as well as methods of Proof and Some Notation, and problems with Equality Constraints.
Abstract: Preface. MATHEMATICAL REVIEW. Methods of Proof and Some Notation. Vector Spaces and Matrices. Transformations. Concepts from Geometry. Elements of Calculus. UNCONSTRAINED OPTIMIZATION. Basics of Set--Constrained and Unconstrained Optimization. One--Dimensional Search Methods. Gradient Methods. Newton's Method. Conjugate Direction Methods. Quasi--Newton Methods. Solving Ax = b. Unconstrained Optimization and Neural Networks. Genetic Algorithms. LINEAR PROGRAMMING. Introduction to Linear Programming. Simplex Method. Duality. Non--Simplex Methods. NONLINEAR CONSTRAINED OPTIMIZATION. Problems with Equality Constraints. Problems with Inequality Constraints. Convex Optimization Problems. Algorithms for Constrained Optimization. References. Index.
3,283 citations
TL;DR: Difficulty connected with solving the general nonlinear programming problem is discussed; several approaches that have emerged in the evolutionary computation community are surveyed; and a set of 11 interesting test cases are provided that may serve as a handy reference for future methods.
Abstract: Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing.
In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.
1,620 citations
TL;DR: This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics that have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas.
Abstract: Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.
646 citations
01 Apr 1996
TL;DR: One of these hybrid methods appears to join the group of state-of-the-art global optimization methods: it combines the reliability properties of the genetic algorithms with the accuracy of Quasi-Newton method, while requiring a computation time only slightly higher than the latter.
Abstract: This paper discusses the trade-off between accuracy, reliability and computing time in global optimization. Particular compromises provided by traditional methods (Quasi-Newton and Nelder-Mead's simplex methods) and genetic algorithms are addressed and illustrated by a particular application in the field of nonlinear system identification. Subsequently, new hybrid methods are designed, combining principles from genetic algorithms and "hill-climbing" methods in order to find a better compromise to the trade-off. Inspired by biology and especially by the manner in which living beings adapt themselves to their environment, these hybrid methods involve two interwoven levels of optimization, namely evolution (genetic algorithms) and individual learning (Quasi-Newton), which cooperate in a global process of optimization. One of these hybrid methods appears to join the group of state-of-the-art global optimization methods: it combines the reliability properties of the genetic algorithms with the accuracy of Quasi-Newton method, while requiring a computation time only slightly higher than the latter.
412 citations
Book•
01 Nov 1996
TL;DR: This chapter discusses local search strategies for the Vehicle Fleet Mix Problem, the Evolution of Solid Object Designs Using Genetic Algorithms, and more.
Abstract: Partial table of contents: Modern Heuristic Techniques. TECHNIQUES. Localized Simulated Annealing in Constraint Satisfaction and Optimization. Observing Logical Interdependencies in Tabu Search: Methods and Results. Reactive Search: Toward Self-Tuning Heuristics. Integrating Local Search into Genetic Algorithms. CASE STUDIES. Local Search for Steiner Trees in Graphs. Local Search Strategies for the Vehicle Fleet Mix Problem. A Tabu Search Algorithm for Some Discrete-Continuous Scheduling Problems. The Analysis of Waste Flow Data from Multi-Unit Industrial Complexes Using Genetic Algorithms. The Evolution of Solid Object Designs Using Genetic Algorithms. The Convoy Movement Problem with Initial Delays. A Brief Comparison of Some Evolutionary Optimization Methods. Index.
388 citations
TL;DR: This paper develops simulated annealing metaheuristics for the vehicle routing and scheduling problem with time window constraints using the λ-interchange mechanism of Osman and thek-node interchange process of Christofides and Beasley.
Abstract: This paper develops simulated annealing metaheuristics for the vehicle routing and scheduling problem with time window constraints. Two different neighborhood structures, the λ-interchange mechanism of Osman and thek-node interchange process of Christofides and Beasley, are implemented. The enhancement of the annealing process with a short-term memory function via a tabu list is examined as a basis for improving the metaheuristic approach. Computational results on test problems from the literature as well as large-scale real-world problem are reported. The metaheuristics achieve solutions that compare favorably with previously reported results.
344 citations
TL;DR: It is illustrated that genetic operators can fulfill long-term strategic functions for a tabu search implementation that is chiefly founded on short-term memory strategies.
Abstract: Some genetic algorithms are considered for the graph coloring problem. As is the case for other combinatorial optimization problems, pure genetic algorithms are outperformed by neighborhood search heuristic procedures such as tabu search. Nevertheless, we examine the performance of several hybrid schemes that can obtain solutions of excellent quality. For some graphs, we illustrate that genetic operators can fulfill long-term strategic functions for a tabu search implementation that is chiefly founded on short-term memory strategies.
330 citations
TL;DR: In this paper, a tutorial introduction to Simulated Annealing, Tabu Search, and Genetic Algorithms is given, as well as a tentative evaluation and comparison from a pragmatic point of view.
282 citations
21 Jul 1996
TL;DR: A relatively new approach to the electromagnetic optimization problem called the genetic algorithm that can handle the common optimization problem characteristics with relative ease yielding near global maxima in cases that cannot be readily handled by any other optimization method.
Abstract: The application of modern electromagnetic theory in real world radiation and scattering problems often requires or at least benefits from the use of optimization. This paper discusses a relatively new approach to the electromagnetic optimization problem called the genetic algorithm that can handle the common optimization problem characteristics with relative ease yielding near global maxima in cases that cannot be readily handled by any other optimization method.
202 citations
22 Sep 1996
TL;DR: This article focuses on the experimental study of the sensitivity of the Ant-Q algorithm to its parameters and on the investigation of synergistic effects when using more than a single ant.
Abstract: Ant-Q is an algorithm belonging to the class of ant colony based methods, that is, of combinatorial optimization methods in which a set of simple agents, called ants, cooperate to find good solutions to combinatorial optimization problems. The main focus of this article is on the experimental study of the sensitivity of the Ant-Q algorithm to its parameters and on the investigation of synergistic effects when using more than a single ant. We conclude comparing Ant-Q with its ancestor Ant System, and with other heuristic algorithms.
Book•
01 Jan 1996
TL;DR: A Finite Algorithm for Global Minimization of Separable Concave Programs and Accelerating Convergence of Branch-and-Bound Algorithms for Quadratically Constrained Optimization Problems.
Abstract: Preface. Lagrange Duality in Partly Convex Programming S. Zlobec. Global Optimization Using Hyperbolic Cross Points E. Novak, K. Ritter. Global Minimization of Separable Concave Functions under Linear Constraints with Totally Unimodular Matrices R. Horst, N. Van Thoai. On Existence of Robust Minimizers S. Shi, et al. A Branch and Bound Algorithm for the Quadratic Assignment Problem Using a Lower Bound Based on Linear Programming K.G. Ramakrishan, et al. Dynamic Matrix Factorization Methods for Using Formulations Derived from Higher Order Lifting Techniques in the Solution of the Quadratic Assignment Problem B. Ramachandran, J.K. Pekny. Conical Coercivity Conditions and Global Minimization on Cones. An Existence Result G. Isac. The Use of Ordinary Differential Equations in Quadratic Maximization with Integer Constraints P. Maponi, et al. Adaptive Control via Non-Convex Optimization G.H. Staus, et al. A Decomposition-Based Global Optimization Approach for Solving Bilevel Linear and Quadratic Problems V. Visweswaran, et al. Generalized TRUST Algorithms for Global Optimization J. Barhen, V. Protopopescu. Test Results for an Interval Branch and Bound Algorithm for Equality-Constrained Optimization R.B. Kearfott. Equivalent Methods for Global Optimization D. MacLagan et al. A C++ Class Library for Interval Arithmetic in Global Optimization K. Holmqvist, A. Migdalas. On the Convergence of Localisation Search D.W. Bulger, G.R. Wood. Stochastic Approximation with Smoothing for Optimization of an Adaptive Recursive Filter W. Edmonson, et al. The Grouping Genetic Algorithm E. Falkenauer. Accelerating Convergence of Branch-and-Bound Algorithms for Quadratically Constrained Optimization Problems T. Van Voorhis, F. Al-Khayyal. Distributed Decomposition-based Approaches in Global Optimization I.P. Androulakis, et al. A Finite Algorithm for Global Minimization of Separable Concave Programs J.P. Shectman, N.V. Sahinidis. A Pseudo e-Approximate Algorithm for Feedback Vertex Set T. Qian, et al. Iterative Topographical Global Optimization A. Torn, S. Viitanen. Global Optimization for the Chemical and Phase Equilibrium Problem Using Interval Analysis K.I.M. McKinnon, et al. Nonconvex Global Optimization of the Separable Resource Allocation Problem with Continuous Variables E. Haddad. A d.c. Approach to the Largest Empty Sphere Problem in Higher Dimension J. Shi, Y. Yoshitsugu. A General D.C. Approach to Location Problems H. Tuy. Global Optimization by Parallel Constrained Biased Random Search I. Garcia, G.T. Herman. Global Optimization Problem in Computer Vision P. Sussner, et al. An Application of Optimization to the Problem of Climate Change J.A. Filar, et al. Dynamic Visualization in Modelling and Optimization of Ill Defined Problems W.F. Eddy, A. Mockus. A New Global Optimization Algorithm for Batch Process Scheduling L. Mockus, G.V. Reklaitis. Nonconvexity and Decent in Nonlinear Programming A. Lucia, J. Xu. Global Optimization of Chemical Processes Using Stochastic Algorithms J.R. Banga, W.D. Seider. Logic-Based Outer- Approximation and Benders Decomposition Algorithms for the Synthesis of Process Networks M. Turkay, I.E. Grossmann. Combinatorially Accelerated Branch-and-Bound Method for Solving the MIP Model of Process Network Synthesis F. Friedler, et al. Discrete Optimization Using String Encodings for the Synthesis of Complete Chemical Processes E.S. Fraga.
01 Jan 1996
TL;DR: The paper shall present brief overviews for the most successful meta-heuristics, and concludes with future directions in this growing area of research.
Abstract: Meta-heuristics are the most recent development in approximate search methods for solving complex optimization problems, that arise in business, commerce, engineering, industry, and many other areas. A meta-heuristic guides a subordinate heuristic using concepts derived from artificial intelligence, biological, mathematical, natural and physical sciences to improve their performance. We shall present brief overviews for the most successful meta-heuristics. The paper concludes with future directions in this growing area of research.
Book•
01 Jan 1996
TL;DR: In this paper, the authors present a meta-heuristic algorithm for the problem of finding the shortest path on a minimum spanning tree in a graph with respect to a set partitioning problem.
Abstract: Meta-Heuristics: An Overview I.H. Osman, J.P. Kelly. Genetic Algorithms: A Parallel Genetic Algorithm for the Set Partitioning Problem D. Levine. Evolutionary Computation and Heuristics Z. Michalewicz. Gene Pool Recombination in Genetic Algorithms H. Muhlenbein, H.-M. Voigt. Genetic and Local Search Algorithms as Robust and Simple Optimization Tools M. Yagiura, T. Ibaraki. Networks and Graphs: Comparison of Heuristic Algorithms for the Degree Constrained Minimum Spanning Tree G. Craig, et al. An Aggressive Search Procedure for the Bipartite Drawing Problem R. Marti. Guided Search for the Shortest Path on Transportation Networks Y.M. Sharaiha, R. Thaiss. Scheduling and Control: A Metaheuristic for the Timetabling Problem H. Abada, E. El-Darzi. Complex Sequencing Problems and Local Search Heuristics P. Brucker, H. Hurink. Heuristic Algorithms for Single Processor Scheduling with Earliness and Flow Time Penalties M. Dell'Amico, et al. Heuristics for the Optimal Control of Thermal Energy Storage G.P. Henze, et al. Exploiting Block Structure to Improve Resource-Constrained Project Schedules H.E. Mausser, S.R. Lawrence. Combining the Large-Step Optimization with Tabu-Search: Application to the Job-Shop Scheduling Problem H. Ramalhinho Lourenco, M. Zwijnenburg. Job-Shop Scheduling by Simulated Annealing Combined with Deterministic Local Search T. Yamada, R. Nakano. Simulated Annealing: Cybernetic Optimization by Simulated Annealing: An Implementation of Parallel Processing Using Probabilistic Feedback Control M.A. Fleischer, S.H. Jacobson. A Simulated Annealing Algorithm for the Computation of Marginal Costs of TelecommunicationLinks J.-L. Lutton, E. Philippart. Learning to Recognize (Un)Promising Simulated Annealing Runs: Efficient Search Procedures for Job Shop Scheduling and Vehicle Routing N.M. Sadeh, S.R. Thangiah. A Preliminary Investigation into the Performance of Heuristic Search Methods Applied to Compound Combinatorial Problems M.B. Wright, R.C. Marett. Tabu Search: Tabu Search, Combination and Integration A.S. Al-Mahmeed. Vector Quantization with the Reactive Tabu Search R. Battiti, et al. Tabu Thresholding for the Frequency Assignment Problem D. Castelino, N. Stephens. A New Tabu Search Approach to the 0-1 Equicut Problem M. Dell'Amico, F. Maffioli. Simple Tabu Thresholding and the Pallet Loading Problem K.A. Dowsland. Critical Event Tabu Search for Multidimensional Knapsack Problems F. Glover, G.A. Kochenberger. Solving Dynamic Stochastic Control Problems in Finance Using Tabu Search with Variable Scaling F. Glover, et al. Comparison of Heuristics for the 0-1 Multidimensional Knapsack Problem S. Hanafi, et al. Probabilistic Move Selection in Tabu Search for Zero-One Mixed Integer Programming Problems A. Lokketangen, F. Glover. A Star- Shaped Diversification Approach in Tabu Search L. Sondergeld, S. Voss. Communication Issues in Designing Cooperative Multi-Thread Parallel Searches M. Toulouse, et al. A Study on Algorithms for Selecting Best Elements from an Array F.T. Tseng. A Modified Tabu Thresholding Approach for the Generalised Restricted Vertex Colouring Problem V. Valls, et al. Chunking Applied to Reactive Tabu Search D.L. Woodruff. Tabu Search on the Geometric Traveling Salesman Problem M. Zachariasen, M. Dam. Traveling Salesman Problems: Mixing Different Components of Metaheuristics
19 Aug 1996
TL;DR: A novel way of looking at local search algorithms for combinatorial optimization problems which better suits constraint programming by performing branch- and-bound search at their core is proposed and a framework described yields a more efficient local search and opens the door to more elaborate neighborhoods.
Abstract: We propose in this paper a novel way of looking at local search algorithms for combinatorial optimization problems which better suits constraint programming by performing branch- and-bound search at their core. We concentrate on neighborhood exploration and show how the framework described yields a more efficient local search and opens the door to more elaborate neighborhoods. Numerical results are given in the context of the traveling salesman problem with time windows. This work on neighborhood exploration is part of ongoing research to develop constraint programming tabu search algorithms applied to routing problems.
TL;DR: In this paper, simulated annealing and tabu search were used to solve the large combinatorial optimization problem of finding an optimal flow or transport parameter that varies spatially.
TL;DR: This paper compares SIMANN to the DFP algorithm on another optimization problem, namely, the maximum likelihood estimation of a rational expectations model, which was previously studied in the literature and shows several advantages over DFP.
Abstract: This paper describes SIMANN, a Fortran and GAUSS implementation of the simulated annealing algorithm. The Fortran code was used in "Global Optimization of Statistical Functions with Simulated Annealing" (Goffe, Ferrier, and Rogers 1994). In that paper, simulated annealing was found to be competitive, if not superior, to multiple restarts of conventional optimization routines for difficult optimization problems. This paper compares SIMANN to the DFP algorithm on another optimization problem, namely, the maximum likelihood estimation of a rational expectations model, which was previously studied in the literature. SIMANN again performs quite well, and shows several advantages over DFP. This paper also describes simulated annealing, and gives explicit directions and an example for using the included GAUSS and Fortran code.
TL;DR: In this article, a solenoidal superconducting magnetic energy storage with active and passive shielding has been optimized by means of different optimization procedures based on the global search algorithm, evolution strategies, simulated annealing and conjugate gradient method, all coupled to integral or finite element codes.
Abstract: A proposal for benchmark problems to test electromagnetic optimization methods, relevant to multiobjective optimization of a solenoidal superconducting magnetic energy storage with active and passive shielding is presented. The system has been optimized by means of different optimization procedures based on the global search algorithm, evolution strategies, simulated annealing and the conjugate gradient method, all coupled to integral or finite element codes. A comparison of results is performed and the features of the problem as a test of optimization procedures are discussed.
TL;DR: In this article, a novel algorithm for the global optimization of functions (C-RTS) is presented, in which a combinatorial optimization method cooperates with a stochastic local minimizer.
Abstract: A novel algorithm for the global optimization of functions (C-RTS) is presented, in which a combinatorial optimization method cooperates with a stochastic local minimizer. The combinatorial optimization component, based on the Reactive Tabu Search recently proposed by the authors, locates the most promising “boxes”, in which starting points for the local minimizer are generated. In order to cover a wide spectrum of possible applications without user intervention, the method is designed with adaptive mechanisms: the box size is adapted to the local structure of the function to be optimized, the search parameters are adapted to obtain a proper balance of diversification and intensification. The algorithm is compared with some existing algorithms, and the experimental results are presented for a variety of benchmark tasks.
Journal Article•
TL;DR: A cultural algorithm based testbed which allows one to plug and play various combinations of evolution components for solving constrained numerical optimization suggests that the belief space is an important contributor to the problem solving process for both systems when the number of constraints on the problem become large enough.
Abstract: This paper introduce a cultural algorithm based testbed which allows one to plug and play various combinations of evolution components for solving constrained numerical optimization. Our cultural algorithm framework combines weak search method with knowledge representation scheme for collecting and reasoning knowledge about individual experience. Currently genetic algorithm based software package GENOCOP(GEnetic algorithm for Numerical Optimization for COnstrained Problems) and rudimentary EP(Evolutionary Programming) are embedded in the cultural algorithm framework. Preliminary results suggest that the belief space is an important contributor to the problem solving process for both systems when the number of constraints on the problem become large enough.
TL;DR: In this paper, an adaptive search method related to the Tabu Search metaheuristic was proposed to solve the linear Bilevel Programming problem. But the results on large scale linear BLPs are limited.
Abstract: The linear Bilevel Programming Problem (BLP) is an instance of a linear hierarchical decision process where the lower level constraint set is dependent on decisions taken at the upper level. In this paper we propose to solve this NP-hard problem using an adaptive search method related to the Tabu Search metaheuristic. Numerical results on large scale linear BLPs are presented.
TL;DR: In this article, a new tabu search metaheuristic is proposed to solve the facility layout of a manufacturing system, usually formulated and solved as a quadratic assignment problem (QAP).
Abstract: The design of the facility layout of a manufacturing system, usually formulated and solved as a quadratic assignment problem (QAP), is of tremendous importance for its effective utilization. In this paper we discuss a new implementation of the tabu search metaheuristic to solve the QAP. Our tabu search implementation includes recency-based and long term memory structure, dynamic tabu list size strategies, and intensification and diversification strategies. The tabu search algorithm converges with a reasonable speed from any random initial solution to very good layouts. Our extensive computational experiments, including statistical analysis and library analysis, strongly support the superiority of our tabu search implementation (we refer to it as (CK)) over existing algorithms in the literature.
TL;DR: This work replaces this manual search by Genetic Programming, a method based on natural evolution, by optimizing the annealing schedule for a well-known combinatorial optimization problem, the Quadratic Assignment Problem.
TL;DR: Local search techniques like simulated annealing and tabu search are based on a neighborhood structure defined on a set of feasible solutions of a discrete optimization problem that allows local search on the set of solutions that are locally optimal.
TL;DR: An introduction to genetic algorithms and their use in the solution of both classical and practical operational research problems, identifies some of the reasons why they have been slow to find widespread appeal, and goes on to show that many of these reasons are gradually being eroded.
Abstract: Compared with other metaheuristic techniques such as simulated annealing and tabu search, research into the use of genetic algorithms for the solution of OR problems is still in its infancy. This paper provides an introduction to genetic algorithms and their use in the solution of both classical and practical operational research problems, identifies some of the reasons why they have been slow to find widespread appeal, and goes on to show that many of these reasons are gradually being eroded.
01 Jan 1996
TL;DR: The surprising variety of continuous approaches reveal interesting theoretical properties which can be explored to develop new algorithms for computing (sub)optimal solutions to discrete optimization problems.
Abstract: This paper contains expository notes about continuous approaches to several discrete optimization problems. There are many ways to formulate discrete problems as equivalent continuous problems or to embed the discrete feasible domain in a larger continuous space (relaxation). The surprising variety of continuous approaches reveal interesting theoretical properties which can be explored to develop new algorithms for computing (sub)optimal solutions to discrete optimization problems.
TL;DR: An experimental comparison of four iterative improvement techniques for schedule optimization that differ in the local search methodology are described, which are iterative deepening, random search, tabu search and genetic algorithms.
28 Jul 1996
TL;DR: This work adapts the genetic programming paradigm to the requirements of the query optimization problem, showing that the nature of the problem makes genetic programming a particularly attractive approach to it.
Abstract: Database query optimization is a hard research problem. Exhaustive techniques are adequate for trivial instances only, while combinatorial optimization techniques are vulnerable to the peculiarities of specific instances. We propose a model based on genetic programming to address this problem, motivated by its robustness and efficiency in a wide area of search problems. We adapt the genetic programming paradigm to the requirements of the query optimization problem, showing that the nature of the problem makes genetic programming a particularly attractive approach to it.
TL;DR: A hybrid of evolutionary programming and a deterministic optimization procedure is applied to a series of non-linear and quadratic optimization problems and indicates that the hybrid method can outperform the other methods when addressing heavily constrained optimization problems in terms of computational efficiency and solution accuracy.
Abstract: A hybrid of evolutionary programming (EP) and a deterministic optimization procedure is applied to a series of non-linear and quadratic optimization problems. The hybrid scheme is compared with other existing schemes such as EP alone, two-phase (TP) optimization, and EP with a non-stationary penalty function (NS-EP). The results indicate that the hybrid method can outperform the other methods when addressing heavily constrained optimization problems in terms of computational efficiency and solution accuracy.