Showing papers on "Heuristic (computer science) published in 1997"

Local Search in Combinatorial Optimization

Abstract: From the Publisher: In the past three decades local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization. Local search is still the method of choice for NP-hard problems as it provides a robust approach for obtaining high-quality solutions to problems of a realistic size in a reasonable time. This area of discrete mathematics is of great practical use and is attracting ever increasing attention. The contributions to this book cover local search and its variants from both a theoretical and practical point of view, each with a chapter written by leading authorities on that particular aspect. This book is an important reference volume and an invaluable source of inspiration for advanced students and researchers in discrete mathematics, computer science, operations research, industrial engineering and management science.

Meaningful change detection in structured data

Abstract: Detecting changes by comparing data snapshots is an important requirement for difference queries, active databases, and version and configuration management. In this paper we focus on detecting meaningful changes in hierarchically structured data, such as nested-object data. This problem is much more challenging than the corresponding one for relational or flat-file data. In order to describe changes better, we base our work not just on the traditional “atomic” insert, delete, update operations, but also on operations that move an entire sub-tree of nodes, and that copy an entire sub-tree. These operations allows us to describe changes in a semantically more meaningful way. Since this change detection problem is NP-hard, in this paper we present a heuristic change detection algorithm that yields close to “minimal” descriptions of the changes, and that has fewer restrictions than previous algorithms. Our algorithm is based on transforming the change detection problem to a problem of computing a minimum-cost edge cover of a bipartite graph. We study the quality of the solution produced by our algorithm, as well as the running time, both analytically and experimentally.

Multi-Sensor Fusion: Fundamentals and Applications With Software

Abstract: I. INTRODUCTION TO SENSOR FUSION. 1. Introduction. Importance. Sensor Processes. Applications. Summary. Problem Set 1. II. FOUNDATIONS OF SENSOR FUSION. 2. Sensors. Mathematical Description. Use of Multiple Sensors. Construction of Reliable Abstract Sensors From Simple Abstract Sensors. Static and Dynamic Networks. Conclusion. Problem Set 2. 3. Mathematical Tools Used. Algorithms. Linear Algebra. Coordinate Transformations. Rigid Body Motion. Probability. Dependability and Markov Chains. Gaussian Noise. Meta-Heuristics. Summary. Problem Set 3. 4. High-Performance Data Structures: CAD Based. Boundary Representations. Sweep Presentation. CSG - Constructive Solid Geometry. Wire-Frame Models and the Wing-Edge Data Structure. Surface Patches and Contours. Generalized Cylinders. Summary. Problem Set 4. 5. High-Performance Data Structures: Tessellated. Sparse Arrays. Simplex Grids of Non-Uniform Sizes. Grayscale and Color Arrays. Occupancy Grids and HIMM Histogram Maps. Summary. Problem Set 5. 6. High-Performance Data Structures: Trees, and Graphs. 2n Trees. Forest of Quadtrees. Translation Invariant Data Structure. Multi-Dimensional Trees. Graphs of Free Space. Description Trees of Polygons. Range and Interval Trees. Summary. Problem Set 6. 7. High-Performance Data Structures: Functions. Interpolation. Least Squares Estimation. Splines. Bezier Curves and Bi-Cubic Patches. Fourier Transform, Cepstrum and Wavelets. Modal Representation. Summary. Problem Set 7. 8. Representing Ranges and Uncertainty in Data Structures. Explicit Accuracy Bounds. Probability and Dempster-Shafer Methods. Statistics. Fuzzy Sets. Summary. Problem Set 8. III. APPLICATIONS TO SENSOR FUSION. 9. Image Registration for Sensor Fusion. Image Registration Techniques. Problem Statement. Fitness Function. Tabu Search. Genetic Algorithms. Simulated Annealing. Results. Summary. 10. Designing Optimal Sensor Systems within Dependability Bounds. Applications. Dependability Measures. Optimization Model. Exhaustive Search on the Multidimensional Surface. Experimental Results of the Exhaustive Search Algorithm. Heuristic Methods. Summary. 11. Sensor Fusion and Approximate Agreement. Byzantine Generals Problem. Approximate Byzantine Matching. Fusion of Contradictory Sensor Information. Performance Comparison. Hybrid Algorithm. Example 1. Example 2. Summary. 12. Kalman Filtering Applied to a Sensor Fusion Problem. Background. A New Method. A New Technique for Cloud Removal. A Prototype System. Kalman Filter for Scenario 1. Discussion of Results. Summary. 13. Optimal Sensor Fusion Using Range Trees Recursively. Sensors. Redundancy and Associated Errors. Faulty Sensor Averaging Problem. Interval Trees. Algorithm to Find the Optimal Region. Algorithm Complexity. Comparison with Known Methods. Summary. 14. Distributed Dynamic Sensor Fusion. Problem Description. New Paradigm for Distributed Dynamic Sensor Fusion. Robust Agreement Using the Optimal Region. A Comparison with Existing Approaches. Experimental Results. Summary. IV. CASE STUDIES AND CONCLUSION. 15. Sensor Fusion Case Studies. Levels of Sensor Fusion. Types of Sensors Available. Research Trends. Case Studies. Summary. 16. Conclusion. Review. Conclusion. Appendix A. Program Source Code. References. Index483.

Heuristic and randomized optimization for the join ordering problem

Abstract: Recent developments in database technology, such as deductive database systems, have given rise to the demand for new, cost-effective optimization techniques for join expressions. In this paper many different algorithms that compute approximate solutions for optimizing join orders are studied since traditional dynamic programming techniques are not appropriate for complex problems. Two possible solution spaces, the space of left-deep and bushy processing trees, are evaluated from a statistical point of view. The result is that the common limitation to left-deep processing trees is only advisable for certain join graph types. Basically, optimizers from three classes are analysed: heuristic, randomized and genetic algorithms. Each one is extensively scrutinized with respect to its working principle and its fitness for the desired application. It turns out that randomized and genetic algorithms are well suited for optimizing join expressions. They generate solutions of high quality within a reasonable running time. The benefits of heuristic optimizers, namely the short running time, are often outweighed by merely moderate optimization performance.

Multicast routing with end-to-end delay and delay variation constraints

Abstract: We study the problem or constructing multicast trees to meet the quality of service requirements of real-time interactive applications operating in high-speed packet-switched environments. In particular, we assume that multicast communication depends on: (1) bounded delay along the paths from the source to each destination and (2) bounded variation among the delays along these paths. We first establish that the problem of determining such a constrained tree is NP-complete. We then present a heuristic that demonstrates good average case behavior in terms of the maximum interdestination delay variation. The heuristic achieves its best performance under conditions typical of multicast scenarios in high speed networks. We also show that it is possible to dynamically reorganize the initial tree in response to changes in the destination set, in a way that is minimally disruptive to the multicast session.

Evaluation of multicast routing algorithms for real-time communication on high-speed networks

Abstract: Multicast (MC) routing algorithms capable of satisfying the quality of service (QoS) requirements of real-time applications will be essential for future high-speed networks. We compare the performance of all of the important MC routing algorithms when applied to networks with asymmetric link loads. Each algorithm is judged based on the quality of the MC trees it generates and its efficiency in managing the network resources. Simulation results over random networks show that unconstrained algorithms are not capable of fulfilling the QoS requirements of real-time applications in wide-area networks. Simulations also reveal that one of the unconstrained algorithms, reverse path multicasting (RPM), is quite inefficient when applied to asymmetric networks. We study how combining routing with resource reservation and admission control improves the RPM's efficiency in managing the network resources. The performance of one semiconstrained heuristic, MSC, three constrained Steiner tree (CST) heuristics, Kompella, Pasquale, and Polyzos (1992), constrained adaptive ordering (CAO), and bounded shortest multicast algorithm (BSMA), and one constrained shortest path tree (CSPT) heuristic, the constrained Dijkstra heuristic (CDKS) are also studied. Simulations show that the semiconstrained and constrained heuristics are capable of successfully constructing MC trees which satisfy the QoS requirements of real-time traffic. However, the cost performance of the heuristics varies. The BSMA's MC trees are lower in cost than all other constrained heuristics. Finally, we compare the execution times of all algorithms, unconstrained, semiconstrained, and constrained.

A Genetic Algorithm for the Multiple-Choice Integer Program

Abstract: We present a genetic algorithm for the multiple-choice integer program that finds an optimal solution with probability one though it is typically used as a heuristic. General constraints are relaxed by a nonlinear penalty function for which the corresponding dual problem has weak and strong duality. The relaxed problem is attacked by a genetic algorithm with solution representation special to the multiple-choice structure. Nontraditional reproduction, crossover and mutation operations are employed. Extensive computational tests for dual degenerate problem instances show that suboptimal solutions can be obtained with the genetic algorithm within running times that are shorter than those of the OSL optimization routine.

Heuristic Algorithms for the Handicapped Persons Transportation Problem

Abstract: We examine the problem of determining an optimal schedule for a fleet of vehicles used to transport handicapped persons in an urban area. The problem is a generalization of the well-known advance-request Pickup and Delivery Problem with Time Windows. Due to the high level of service required by this kind of transport, several additional operational constraints must be considered. The problem is NP-hard in the strong sense, and exact approaches for the solution of real-life problems (typically with hundreds of users to be transported) are not practicable. We describe a fast and effective parallel insertion heuristic algorithm which is able to determine good solutions for real-world instances of the problem in a few seconds on a personal computer. We also present a Tabu Thresholding procedure which can be used to improve the starting solution obtained by the insertion algorithm. The application of the proposed procedures to a set of real-life instances for the city of Bologna, involving about 300 trips each...

SALOME: A Bidirectional Branch-and-Bound Procedure for Assembly Line Balancing

Abstract: In this article, we report on new results for the well-known Simple Assembly Line Balancing Problem Type 1. For this NP-hard problem, a large number of exact and heuristic algorithms have been proposed in the last four decades. Recent research has led to efficient branch-and-bound procedures. Based on an analysis of their specific strengths, a new algorithm (Salome) is developed. Its main characteristic is a new branching strategy (local lower-bound method) and a bidirectional branching rule. Furthermore, new bounding and dominance rules are included. Computational experiments on the basis of former data sets, as well as a new, more challenging one, show that Salome outperforms the most effective existing procedures for solving this problem.

Distribution network reconfiguration for energy loss reduction

Abstract: A new method for energy loss reduction in distribution networks is presented It is based on known techniques and algorithms for radial network analysis-oriented element ordering, power summation method for power flow, statistical representation of load variations and a recently developed energy summation method for the computation of energy losses These methods, combined with the heuristic rules developed to lead the iterative process, make the energy loss minimization method effective, robust and fast It presents an alternative to the power minimization methods for operation and planning purposes

A Grasp for Aircraft Routing in Response to Groundings and Delays

Abstract: This paper presents a greedy randomized adaptive search procedure (GRASP) to reconstruct aircraft routings in response to groundings and delays experienced over the course of the day. Whenever the schedule is disrupted, the immediate objective of the airlines is to minimize the cost of reassigning aircraft to flights taking into account available resources and other system constraints. Associated costs are measured by flight delays and cancellations. In the procedure, the neighbors of an incumbent solution are generated and evaluated, and the most desirable are placed on a restricted candidate list. One is selected randomly and becomes the incumbent. The heuristic is polynomial with respect to the number of flights and aircraft. This is reflected in our computational experience with data provided by Continental Airlines. Empirical results demonstrate the ability of the GRASP to quickly explore a wide range of scenarios and, in most cases, to produce an optimal or near-optimal solution.

SALOME. a bidirectional branch and bound procedure for assembly line balancing

Abstract: In this article, we report on new results for the well-known Simple Assembly Line Balancing Problem Type 1. For this NP-hard problem, a large number of exact and heuristic algorithms have been proposed in the last four decades. Recent research has led to efficient branch-and-bound procedures. Based on an analysis of their specific strengths, a new algorithm (Salome) is developed. Its main characteristic is a new branching strategy (local lower-bound method) and a bidirectional branching rule. Furthermore, new bounding and dominance rules are included. Computational experiments on the basis of former data sets, as well as a new, more challenging one, show that Salome outperforms the most effective existing procedures for solving this problem.

Efficient optimization of all-terminal reliable networks, using an evolutionary approach

Abstract: The use of computer communication networks has been rapidly increasing in order to: (1) share expensive hardware and software resources, and (2) provide access to main system from distant locations. The reliability and cost of these systems are important and are largely determined by network topology. Network topology consists of nodes and the links between nodes. The selection of optimal network topology is an NP-hard combinatorial problem so that the classical enumeration-based methods grow exponentially with network size. In this study, a heuristic search algorithm inspired by evolutionary methods is presented to solve the all-terminal network design problem when considering cost and reliability. The genetic algorithm heuristic is considerably enhanced over conventional implementations to improve effectiveness and efficiency. This general optimization approach is computationally efficient and highly effective on a large suite of test problems with search spaces up to 2/spl middot/10/sup 90/.

HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem

Abstract: We present HAS-SOP, a new approach to solving sequential ordering problems. HAS-SOP combines the ant colony algorithm, a population-based metaheuristic, with a new local optimizer, an extension of a TSP heuristic which directly handles multiple constraints without increasing computational complexity. We compare different implementations of HAS-SOP and present a new data structure that improves system performance. Experimental results on a set of twenty-three test problems taken from the TSPLIB show that HAS-SOP outperforms existing methods both in terms of solution quality and computation time. Moreover, HAS-SOP improves most of the best known results for the considered problems.

Capacitor placement, replacement and control in large-scale distribution systems by a GA-based two-stage algorithm

Abstract: This paper presents a two-stage algorithm tailored for capacitor placement, replacement and control of general, large-scale, unbalanced distribution systems. The first stage of the proposed algorithm consists of a GA followed by the second stage which consists of a sensitivity-based heuristic method tailored for the capacitor placement, replacement and control problem. The two-stage algorithm is designed to take advantage of the merits of each technique. The GA is employed to find neighborhoods of high quality solutions and to provide a good initial guess for the sensitivity-based heuristic. The heuristic uses the sensitivity of real power loss to reactive power to quickly and locally improve upon the solution provided by the GA with less computation than by allowing the GA to continue. The two-stage algorithm was implemented in the C programming language and tested for a 292 bus unbalanced system with single, two and three-phase branches and grounded and ungrounded portions of the network with promising results.

Static and dynamic channel assignment using neural networks

Abstract: We examine the problem of assigning calls in a cellular mobile network to channels in the frequency domain. Such assignments must be made so that interference between calls is minimized, while demands for channels are satisfied. A new nonlinear integer programming representation of the static channel assignment (SCA) problem is formulated. We then propose two different neural networks for solving this problem. The first is an improved Hopfield (1982) neural network which resolves the issues of infeasibility and poor solution quality which have plagued the reputation of the Hopfield network. The second approach is a new self-organizing neural network which is able to solve the SCA problem and many other practical optimization problems due to its generalizing ability. A variety of test problems are used to compare the performance of the neural techniques against more traditional heuristic approaches. Finally, extensions to the dynamic channel assignment problem are considered.

Distribution network reconfiguration for loss reduction using fuzzy controlled evolutionary programming

Abstract: Network reconfiguration for loss reduction in distribution systems is a very important way to save energy. However, due to its nature it is an inherently difficult optimisation problem. A new type of evolutionary search technique, evolutionary programming (EP), has been adopted and improved for this particular application. To improve the performance of EP, a fuzzy controlled EP (FCEP), based on heuristic information, is first proposed. The mutation fuzzy controller adaptively adjusts the mutation rate during the simulated evolutionary process. The status of each switch in distribution systems is naturally represented by a binary control parameter 0 or 1. The length of string is much shorter than those proposed by others. A chain-table and combined depth-first and breadth-first search strategy is employed to further speed up the optimisation process. The equality and inequality constraints are imbedded into the fitness function by penalty factors which guarantee the optimal solutions searched by the FCEP are feasible. The implementation of the proposed FCEP for feeder reconfiguration is described in detail. Numerical results are presented to illustrate the feasibility of the proposed FCEP.

Task allocation algorithms for maximizing reliability of distributed computing systems

Abstract: We consider the problem of finding an optimal and suboptimal task allocation (i.e., to which processor should each module of a task or program be assigned) in distributed computing systems with the goal of maximizing the system reliability (i.e., the probability that the system can run the entire task successfully). The problem of finding an optimal task allocation is known to be NP-hard in the strong sense. We present an algorithm for this problem, which uses the idea of branch and bound with underestimates for reducing the computations in finding an optimal task allocation. The algorithm reorders the list of modules to allow a subset of modules that do not communicate with one another to be assigned last, for further reduction in the computations of optimal task allocation for maximizing reliability. We also present a heuristic algorithm which obtains suboptimal task allocations in a reasonable amount of computational time. We study the performance of the algorithms over a wide range of parameters such as the number of modules, the number of processors, the ratio of average execution cost to average communication cost, and the connectivity of modules. We demonstrate the effectiveness of our algorithms by comparing them with recent competing task allocation algorithms for maximizing reliability available in the literature.

Approximate Signal Processing

Abstract: It is increasingly important to structure signal processing algorithms and systems to allow for trading off between the accuracy of results and the utilization of resources in their implementation. In any particular context, there are typically a variety of heuristic approaches to managing these tradeoffs. One of the objectives of this paper is to suggest that there is the potential for developing a more formal approach, including utilizing current research in Computer Science on Approximate Processing and one of its central concepts, Incremental Refinement. Toward this end, we first summarize a number of ideas and approaches to approximate processing as currently being formulated in the computer science community. We then present four examples of signal processing algorithms/systems that are structured with these goals in mind. These examples may be viewed as partial inroads toward the ultimate objective of developing, within the context of signal processing design and implementation, a more general and rigorous framework for utilizing and expanding upon approximate processing concepts and methodologies.

A parallel station heuristic for the mixed-model production line balancing problem

Abstract: Demand for customized products and proliferation of optimal features have increased the need for flexible assembly systems that are capable of simultaneously producing multiple versions of similar products. Serial assembly systems have traditionally been used for the production of a single product type, and more recently for mixed model production. In this paper, a nonlinear integer program is proposed as a model for the production line balancing problem (PLBP). This problem entails the assignment of tasks to stages in a serial production line. The model allows mixed-model production and the use of identical parallel workstations at each stage of the serial production system. The objective function trades off idle workstation time with duplication of task-dependent equipment/ tooling cost. A heuristic is developed to create parallel workstations and assign tasks. Station utilization is also explicitly considered by using a threshold variable for target (acceptable) levels. The procedure is illustrated wit...

Simulated Annealing and Genetic Algorithms for the Facility LayoutProblem: A Survey

Abstract: The facility layout problem (FLP) has many practical applications and is known to be NP-hard. During recent decades exact and heuristic approaches have been proposed in the literature to solve FLPs. In this paper we review the most recent developments regarding simulated annealing and genetic algorithms for solving facility layout problems approximately.

A Tabu Search Procedure to Solve MultiObjective Combinatorial Optimization Problems

Abstract: Several studies have considered metaheuristics, especially simulated annealing, for solving combinatorial optimization problems involving several objectives. Yet, few works have been devoted to tabu search approaches. In this paper, we present a heuristic based upon tabu search principles to generate a good approximation of the set of the Pareto-optimal (efficient) solutions.

Robust search algorithms for test pattern generation

Abstract: In recent years several highly effective algorithms have been proposed for Automatic Test Pattern Generation (ATPG). Nevertheless, most of these algorithms too often rely on different types of heuristics to achieve good empirical performance. Moreover there has not been significant research work on developing algorithms that are robust, in the sense that they can handle most faults with little heuristic guidance. In this paper we describe an algorithm for ATPG that is robust and still very efficient. In contrast with existing algorithms for ATPG, the proposed algorithm reduces heuristic knowledge to a minimum and relies on an optimized search algorithm for effectively pruning the search space. Even though the experimental results are obtained using an ATPG tool built on top of a Propositional Satisfiability (SAT) algorithm, the same concepts can be integrated on application-specific algorithms.

A scatter search based approach for the quadratic assignment problem

Abstract: Scatter search is an evolutionary heuristic, proposed two decades ago, that uses linear combinations of a population subset to create new solutions. A special operator is used to ensure their feasibility and to improve their quality. The authors propose a scatter search approach to the QAP problem. The basic method is extended with intensification and diversification stages and they present a procedure to generate good scattered initial solutions.

Solving hard qualitative temporal reasoning problems: Evaluating the efficiency of using the ORD-Horn class

Abstract: While the worst-case computational properties of Allen's calculus for qualitative temporal reasoning have been analyzed quite extensively, the determination of the empirical efficiency of algorithms for solving the consistency problem in this calculus has received only little research attention. In this paper, we will demonstrate that using the ORD-Horn class in Ladkin and Reinefeld's backtracking algorithm leads to performance improvements when deciding consistency of hard instances in Allen's calculus. For this purpose, we prove that Ladkin and Reinefeld's algorithm is complete when using the ORD-Horn class, we identify phase transition regions of the reasoning problem, and compare the improvements of ORD-Horn with other heuristic methods when applied to instances in the phase transition region. Finally, we give evidence that combining search methods orthogonally can dramatically improve the performance of the backtracking algorithm.