Showing papers on "Heuristic (computer science) published in 1999"

The ant system applied to the quadratic assignment problem

Abstract: In recent years, there has been growing interest in algorithms inspired by the observation of natural phenomena to define computational procedures that can solve complex problems. We describe a distributed heuristic algorithm that was inspired by the observation of the behavior of ant colonies, and we propose its use for the quadratic assignment problem. The results obtained in solving several classical instances of the problem are compared with those obtained from other evolutionary heuristics to evaluate the quality of the proposed system.

Test‐data generation using genetic algorithms

Abstract: This paper presents a technique that uses a genetic algorithm for automatic test-data generation. A genetic algorithm is a heuristic that mimics the evolution of natural species in searching for the optimal solution to a problem. In the test-data generation application, the solution sought by the genetic algorithm is test data that causes execution of a given statement, branch, path, or definition–use pair in the program under test. The test-data-generation technique was implemented in a tool called TGen, in which parallel processing was used to improve the performance of the search. To experiment with TGen, a random test-data generator called Random was also implemented. Both Tgen and Random were used to experiment with the generation of test-data for statement and branch coverage of six programs. Copyright © 1999 John Wiley & Sons, Ltd.

A Heuristic Method for the Set Covering Problem

Abstract: We present a Lagrangian-based heuristic for the well-known Set Covering Problem (SCP). The algorithm was initially designed for solving very large scale SCP instances, involving up to 5,000 rows an...

Temporal sequence learning and data reduction for anomaly detection

Abstract: The anomaly-detection problem can be formulated as one of learning to characterize the behaviors of an individual, system, or network in terms of temporal sequences of discrete data. We present an approach on the basis of instance-based learning (IBL) techniques. To cast the anomaly-detection task in an IBL framework, we employ an approach that transforms temporal sequences of discrete, unordered observations into a metric space via a similarity measure that encodes intra-attribute dependencies. Classification boundaries are selected from an a posteriori characterization of valid user behaviors, coupled with a domain heuristic. An empirical evaluation of the approach on user command data demonstrates that we can accurately differentiate the profiled user from alternative users when the available features encode sufficient information. Furthermore, we demonstrate that the system detects anomalous conditions quickly — an important quality for reducing potential damage by a malicious user. We present several techniques for reducing data storage requirements of the user profile, including instance-selection methods and clustering. As empirical evaluation shows that a new greedy clustering algorithm reduces the size of the user model by 70%, with only a small loss in accuracy.

Heuristic and Metaheuristic Approaches for a Class of Two-Dimensional Bin Packing Problems

Abstract: Two-dimensional bin packing problems consist of allocating, without overlapping, a given set of small rectangles (items) to a minimum number of large identical rectangles (bins), with the edges of the items parallel to those of the bins. According to the specific application, the items may either have a fixed orientation or they can be rotated by 90°. In addition, it may or not be imposed that the items are obtained through a sequence of edge-to-edge cuts parallel to the edges of the bin. In this article, we consider the class of problems arising from all combinations of the above requirements. We introduce a new heuristic algorithm for each problem in the class, and a unified tabu search approach that is adapted to a specific problem by simply changing the heuristic used to explore the neighborhood. The average performance of the single heuristics and of the tabu search are evaluated through extensive computational experiments.

Efficient and Extensible Algorithms for Multi Query Optimization

Abstract: Complex queries are becoming commonplace, with the growing use of decision support systems. These complex queries often have a lot of common sub-expressions, either within a single query, or across multiple such queries run as a batch. Multi-query optimization aims at exploiting common sub-expressions to reduce evaluation cost. Multi-query optimization has hither-to been viewed as impractical, since earlier algorithms were exhaustive, and explore a doubly exponential search space. In this paper we demonstrate that multi-query optimization using heuristics is practical, and provides significant benefits. We propose three cost-based heuristic algorithms: Volcano-SH and Volcano-RU, which are based on simple modifications to the Volcano search strategy, and a greedy heuristic. Our greedy heuristic incorporates novel optimizations that improve efficiency greatly. Our algorithms are designed to be easily added to existing optimizers. We present a performance study comparing the algorithms, using workloads consisting of queries from the TPC-D benchmark. The study shows that our algorithms provide significant benefits over traditional optimization, at a very acceptable overhead in optimization time.

Solution algorithms for the capacitated single allocation hub location problem

Abstract: In this paper, we present an efficient approach for solving capacitated single allocationhub location problems We use a modified version of a previous mixed integer linearprogramming formulation developed by us for p‐hub median problems This formulationrequires fewer variables and constraints than those traditionally used in the literature Wedevelop good heuristic algorithms for its solution based on simulated annealing (SA) andrandom descent (RDH) We use the upper bound to develop an LP‐based branch and boundsolution method The problem, as we define it, finds applications in the design of postaldelivery networks, particularly in the location of capacitated mail sorting and distributioncentres We test our algorithms on data obtained from this application To the best of ourknowledge, this problem has not been solved in the literature Computational results arepresented indicating the usefulness of our approach

Simulated Annealing for Complex Portfolio Selection Problems

Abstract: This paper describes the application of a simulated annealing approach to the solution of a complex portfolio selection model. The model is a mixed integer quadratic programming problem which arises when Markowitz' classical mean-variance model is enriched with additional realistic constraints. Exact optimization algorithms run into difficulties in this framework and this motivates the investigation of heuristic techniques. Computational experiments indicate that the approach is promising for this class of problems.

Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem

Abstract: This article introduces two new techniques for solving the Quadratic Assignment Problem. The first is a heuristic technique, defined in accordance with the Ant System metaphor, and includes as a distinctive feature the use of a new lower bound at each constructive step. The second is a branch-and-bound exact approach, containing some elements introduced in the Ant algorithm. Computational results prove the effectiveness of both approaches.

Selection of views to materialize under a maintenance cost constraint

Abstract: A data warehouse stores materialized views derived from one or more sources for the purpose of efficiently implementing decision-support or OLAP queries. One of the most important decisions in designing a data warehouse is the selection of materialized views to be maintained at the warehouse. The goal is to select an appropriate set of views that minimizes total query response time and/or the cost of maintaining the selected views, given a limited amount of resource such as materialization time, storage space, or total view maintenance time. In this article, we develop algorithms to select a set of views to materialize in a data warehouse in order to minimize the total query response time under the constraint of a given total view maintenance time. As the above maintenance-cost view-selection problem is extremely intractable, we tackle some special cases and design approximation algorithms. First, we design an approximation greedy algorithm for the maintenance-cost view-selection problem in OR view graphs, which arise in many practical applications, e.g., data cubes. We prove that the query benefit of the solution delivered by the proposed greedy heuristic is within 63% of that of the optimal solution. Second, we also design an A * heuristic, that delivers an optimal solution, for the general case of AND-OR view graphs. We implemented our algorithms and a performance study of the algorithms shows that the proposed greedy algorithm for OR view graphs almost always delivers an optimal solution.

A heuristic nonlinear constructive method for distribution system reconfiguration

Abstract: This reconfiguration algorithm starts with all operable switches open, and at each step, closes the switch that results in the least increase in the objective function. The objective function is defined as incremental losses divided by incremental load served. A simplified loss formula is used to screen candidate switches, but a full load flow after each actual switch closing maintains accurate loss and constraint information. A backtracking option mitigates the algorithm's greedy search. This algorithm takes more computer time than other methods, but it models constraints and control action more accurately. A network load flow is used to provide a lower bound on the losses. The paper includes results on several test systems used by other authors.

Synchronized Development of Production, Inventory, and Distribution Schedules

Abstract: An integrated optimization model for production and distribution planning is proposed, with the aim of optimally coordinating important and interrelated logistic decisions such as capacity management, inventory allocation, and vehicle routing. The integrated model is solved via Lagrangean relaxation and both lower bounds and heuristic feasible solutions are obtained. Computational results on test problems of various sizes are provided to show the effectiveness of the proposed solution scheme. Moreover, the feasible solution obtained is compared to that generated by an alternative decoupled approach in which a production plan is first developed and a distribution schedule is consequently derived. Computational results seem to indicate a substantial advantage of the synchronized approach over the decoupled decision process.

Efficient orderbatching methods in warehouses

Abstract: In this paper, the orderbatching problem in warehouses is investigated. Batching, or clustering orders together in the picking process to form a single picking route, reduces travel time and, as such, this phenomenon can be encountered in many warehouses. The reason for its importance is that orderpicking is a labour intensive process and, by using good batching methods, substantial savings can be obtained. The batching and routing problems are complex to solve. In practice, simple methods are used for the batching problem, such as first-come first-served (FCFS), i.e. combine orders in the route in the arriving sequence until the pickdevice is full. Once clusters of orders have been formed, the calculation of the travel time for the routes requires the solution of a number of travelling salesman problems (one for each route). Two groups of heuristic algorithms are evaluated: the Seed algorithms and the somewhat more complex (and CPU time consuming) Time Savings algorithms. The performance of the algorithm...

Array failure correction with a genetic algorithm

Abstract: A flexible approach using the genetic algorithm (GA) is proposed for array failure correction in digital beamforming of arbitrary arrays. In this approach, beamforming weights of an array are represented directly by a vector of complex numbers. The decimal linear crossover is employed so that no binary coding and decoding is necessary. Three mating schemes, adjacent-fitness-paring (AFP), best-mate-worst (BMW), and emperor-selective (EMS), are proposed and their performances are studied. Near-solutions from other analytic or heuristic techniques may be injected into the initial population to speed up convergence. Numerical examples of single- and multiple-element failure correction are presented to show the effectiveness of the approach.

Dynamic power management based on continuous-time Markov decision processes

Abstract: This paper introduces a continuous-time, controllable Markov process model of a power-managed system. The system model is composed of the corresponding stochastic models of the service queue and the service provider. The system environment is modeled by a stochastic service request process. The problem of dynamic power management in such a system is formulated as a policy optimization problem and solved using an efficient "policy iteration" algorithm. Compared to previous work on dynamic power management, our formulation allows better modeling of the various system components, the power-managed system as a whole, and its environment. In addition it captures dependencies between the service queue and service provider status. Finally, the resulting power management policy is asynchronous, hence it is more power-efficient and more useful in practice. Experimental results demonstrate the effectiveness of our policy optimization algorithm compared to a number of heuristic (time-out and N-policy) algorithms.

Selection of Views to Materialize Under a Maintenance Cost Constraint

Abstract: A data warehouse stores materialized views derived from one or more sources for the purpose of efficiently implementing decision-support or OLAP queries. One of the most important decisions in designing a data warehouse is the selection of materialized views to be maintained at the warehouse. The goal is to select an appropriate set of views that minimizes total query response time and/or the cost of maintaining the selected views, given a limited amount of resource such as materialization time, storage space, or total view maintenance time. In this article, we develop algorithms to select a set of views to materialize in a data warehouse in order to minimize the total query response time under the constraint of a given total view maintenance time. As the above maintenance-cost view-selection problem is extremely intractable, we tackle some special cases and design approximation algorithms. First, we design an approximation greedy algorithm for the maintenance-cost view-selection problem in OR view graphs, which arise in many practical applications, e.g., data cubes. We prove that the query benefit of the solution delivered by the proposed greedy heuristic is within 63% of that of the optimal solution. Second, we also design an A* heuristic, that delivers an optimal solution, for the general case of AND-OR view graphs. We implemented our algorithms and a performance study of the algorithms shows that the proposed greedy algorithm for OR view graphs almost always delivers an optimal solution.

Optimization of constrained frequent set queries with 2-variable constraints

Abstract: Currently, there is tremendous interest in providing ad-hoc mining capabilities in database management systems. As a first step towards this goal, in [15] we proposed an architecture for supporting constraint-based, human-centered, exploratory mining of various kinds of rules including associations, introduced the notion of constrained frequent set queries (CFQs), and developed effective pruning optimizations for CFQs with 1-variable (1-var) constraints.While 1-var constraints are useful for constraining the antecedent and consequent separately, many natural examples of CFQs illustrate the need for constraining the antecedent and consequent jointly, for which 2-variable (2-var) constraints are indispensable. Developing pruning optimizations for CFQs with 2-var constraints is the subject of this paper. But this is a difficult problem because: (i) in 2-var constraints, both variables keep changing and, unlike 1-var constraints, there is no fixed target for pruning; (ii) as we show, “conventional” monotonicity-based optimization techniques do not apply effectively to 2-var constraints.The contributions are as follows. (1) We introduce a notion of quasi-succinctness, which allows a quasi-succinct 2-var constraint to be reduced to two succinct 1-var constraints for pruning. (2) We characterize the class of 2-var constraints that are quasi-succinct. (3) We develop heuristic techniques for non-quasi-succinct constraints. Experimental results show the effectiveness of all our techniques. (4) We propose a query optimizer for CFQs and show that for a large class of constraints, the computation strategy generated by the optimizer is ccc-optimal, i.e., minimizing the effort incurred w.r.t. constraint checking and support counting.

On Rectangular Partitionings in Two Dimensions: Algorithms, Complexity, and Applications

Abstract: Partitioning a multi-dimensional data set into rectangular partitions subject to certain constraints is an important problem that arises in many database applications, including histogram-based selectivity estimation, load-balancing, and construction of index structures. While provably optimal and efficient algorithms exist for partitioning one-dimensional data, the multi-dimensional problem has received less attention, except for a few special cases. As a result, the heuristic partitioning techniques that are used in practice are not well understood, and come with no guarantees on the quality of the solution. In this paper, we present algorithmic and complexity-theoretic results for the fundamental problem of partitioning a two-dimensional array into rectangular tiles of arbitrary size in a way that minimizes the number of tiles required to satisfy a given constraint. Our main results are approximation algorithms for several partitioning problems that provably approximate the optimal solutions within small constant factors, and that run in linear or close to linear time. We also establish the NP-hardness of several partitioning problems, therefore it is unlikely that there are efficient, i.e., polynomial time, algorithms for solving these problems exactly. We also discuss a few applications in which partitioning problems arise. One of the applications is the problem of constructing multi-dimensional histograms. Our results, for example, give an efficient algorithm to construct the V-Optimal histograms which are known to be the most accurate histograms in several selectivity estimation problems. Our algorithms are the first to provide guaranteed bounds on the quality of the solution.

Improved Approximation Algorithms for Capacitated Facility Location Problems

Abstract: In a recent surprising result, Korupolu, Plaxton, and Rajaraman [10,11] showed that a simple local search heuristic for the capacitated facility location problem (CFLP) in which the service costs obey the triangle inequality produces a solution in polynomial time which is within a factor of 8 + Ɛ of the value of an optimal solution. By simplifying their analysis, we are able to show that the same heuristic produces a solution which is within a factor of 6(1 + Ɛ) of the value of an optimal solution. Our simplified analysis uses the supermodularity of the cost function of the problem and the integrality of the transshipment polyhedron. Additionally, we consider the variant of the CFLP in which one may open multiple copies of any facility. Using ideas from the analysis of the local search heuristic, we show how to turn any α-approximation algorithm for this variant into one which, at an additional cost of twice the optimum of the standard CFLP, opens at most one additional copy of any facility. This allows us to transform a recent 3-approximation algorithm of Chudak and Shmoys [5] that opens many additional copies of facilities into a polynomial-time algorithm which only opens one additional copy and has cost no more than five times the value of the standard CFLP.

A genetic algorithm for a 2D industrial packing problem

Abstract: Cutting and packing problems are encountered in many industries, with different industries incorporating different constraints and objectives The wood-, glass- and paper industry are mainly concerned with the cutting of regular figures, whereas in the ship building, textile and leather industry irregular, arbitrary shaped items are to be packed In this paper two genetic algorithms are described for a rectangular packing problem Both GAs are hybridised with a heuristic placement algorithm, one of which is the well-known Bottom-Left routine A second placement method has been developed which overcomes some of the disadvantages of the Bottom-Left rule The two hybrid genetic algorithms are compared with heuristic placement algorithms In order to show the effectiveness of the design of the two genetic algorithms, their performance is compared to random search

Protein structure prediction with evolutionary algorithms

Abstract: Evolutionary algorithms have been successfully applied to a variety of molecular structure prediction problems In this paper we reconsider the design of genetic algorithms that have been applied to a simple protein structure prediction problem Our analysis considers the impact of several algorithmic factors for this problem: the conformational representation, the energy formulation and the way in which infeasible conformations are penalized Further we empirically evaluate the impact of these factors on a small set of polymer sequences Our analysis leads to specific recommendations for both GAs as well as other heuristic methods for solving PSP on the HP model

On rectangular partitionings in two dimensions : Algorithms, complexity, and applications

Abstract: Partitioning a multi-dimensional data set into rectangular partitions subject to certain constraints is an important problem that arises in many database applications, including histogram-based selectivity estimation, load-balancing, and construction of index structures. While provably optimal and efficient algorithms exist for partitioning one-dimensional data, the multi-dimensional problem has received less attention, except for a few special cases. As a result, the heuristic partitioning techniques that are used in practice are not well understood, and come with no guarantees on the quality of the solution. In this paper, we present algorithmic and complexity-theoretic results for the fundamental problem of partitioning a two-dimensional array into rectangular tiles of arbitrary size in a way that minimizes the number of tiles required to satisfy a given constraint. Our main results are approximation algorithms for several partitioning problems that provably approximate the optimal solutions within small constant factors, and that run in linear or close to linear time. We also establish the NP-hardness of several partitioning problems, therefore it is unlikely that there are efficient, i.e., polynomial time, algorithms for solving these problems exactly. We also discuss a few applications in which partitioning problems arise. One of the applications is the problem of constructing multi-dimensional histograms. Our results, for example, give an efficient algorithm to construct the V-Optimal histograms which are known to be the most accurate histograms in several selectivity estimation problems. Our algorithms are the first to provide guaranteed bounds on the quality of the solution.

Theory and Methodology A heuristic algorithm for the symmetric and asymmetric vehicle routing problems with backhauls

Abstract: We consider an extension of the capacitated Vehicle Routing Problem (VRP), known as the Vehicle RoutingProblem with Backhauls (VRPB), in which the set of customers is partitioned into two subsets: Linehaul and Backhaulcustomers. Each Linehaul customer requires the delivery of a given quantity of product from the depot, whereas a givenquantity of product must be picked up from each Backhaul customer and transported to the depot. VRPB is known tobe NP-hard in the strong sense, and many heuristic algorithms were proposed for the approximate solution of theproblem with symmetric or Euclidean cost matrices. We present a cluster-first-route-second heuristic which uses a newclustering method and may also be used to solve problems with asymmetric cost matrix. The approach exploits theinformation of the normally infeasible VRPB solutions associated with a lower bound. The bound used is a Lagrangianrelaxation previously proposed by the authors. The final set of feasible routes is built through a modified TravelingSalesman Problem (TSP) heuristic, and inter-route and intra-route arc exchanges. Extensive computational tests onsymmetric and asymmetric instances from the literature show the e•ectiveness of the proposed approach. O 1999Elsevier Science B.V. All rights reserved.Keywords: Vehicle routing; Lagrangian relaxation; Heuristic algorithms; Local search