Showing papers on "Heuristic (computer science) published in 2003"

Efficient greedy learning of Gaussian mixture models

Abstract: This article concerns the greedy learning of gaussian mixtures. In the greedy approach, mixture components are inserted into the mixture one after the other. We propose a heuristic for searching for the optimal component to insert. In a randomized manner, a set of candidate new components is generated. For each of these candidates, we find the locally optimal new component and insert it into the existing mixture. The resulting algorithm resolves the sensitivity to initialization of state-of-the-art methods, like expectation maximization, and has running time linear in the number of data points and quadratic in the (final) number of mixture components. Due to its greedy nature, the algorithm can be particularly useful when the optimal number of mixture components is unknown. Experimental results comparing the proposed algorithm to other methods on density estimation and texture segmentation are provided.

A Multi-Stage Stochastic Integer Programming Approach for Capacity Expansion under Uncertainty

Abstract: This paper addresses a multi-period investment model for capacity expansion in an uncertain environment. Using a scenario tree approach to model the evolution of uncertain demand and cost parameters, and fixed-charge cost functions to model the economies of scale in expansion costs, we develop a multi-stage stochastic integer programming formulation for the problem. A reformulation of the problem is proposed using variable disaggregation to exploit the lot-sizing substructure of the problem. The reformulation significantly reduces the LP relaxation gap of this large scale integer program. A heuristic scheme is presented to perturb the LP relaxation solutions to produce good quality integer solutions. Finally, we outline a branch and bound algorithm that makes use of the reformulation strategy as a lower bounding scheme, and the heuristic as an upper bounding scheme, to solve the problem to global optimality. Our preliminary computational results indicate that the proposed strategy has significant advantages over straightforward use of commercial solvers.

An efficient clustering-based heuristic for data gathering and aggregation in sensor networks

Abstract: The rapid advances in processor, memory, and radio technology have enabled the development of distributed networks of small, inexpensive nodes that are capable of sensing, computation, and wireless communication Sensor networks of the future are envisioned to revolutionize the paradigm of collecting and processing information in diverse environments However, the severe energy constraints and limited computing resources of the sensors, present major challenges for such a vision to become a reality We consider a network of energy-constrained sensors that are deployed over a region Each sensor periodically produces information as it monitors its vicinity The basic operation in such a network is the systematic gathering and transmission of sensed data gathering and transmission of sensed data to a base station for further processing During data gathering, sensors have the ability to perform in-network aggregation (fusion) of data packets enroute to the base station The lifetime of such a sensor system is the time during which we can gather information from all the sensors to the base station A key challenge in data gathering is to maximize the system lifetime, given the energy constraints of the sensors Given the location of sensors and the base station and the available energy at each sensor, we are interested in finding an efficient manner in which data should be collected from all the sensors and transmitted to the base station, such that the system lifetime is maximized This is the maximum lifetime data-gathering problem In this paper, we describe a heuristic to solve the data-gathering problem with aggregation in sensor networks Our experimental results demonstrate that the proposed algorithm significantly outperform previous methods, in terms of system lifetime

Real-time randomized path planning for robot navigation

Abstract: Mobile robots often find themselves in a situation where they must find a trajectory to another position in their environment, subject to constraints posed by obstacles and the robot's capabilities. This poses the problem of planning a path through a continuous domain. Several approaches have been used to address this problem each with some limitations, including state discretizations, planning efficiency, and lack of interleaved execution. Rapidly-exploring random trees (RRTs) are a recently developed algorithm on which fast continuous domain path planners can be based. In this work, we build a path planning system based on RRTs that interleaves planning and execution, first evaluating it in simulation and then applying it to physical robots. Our algorithm, ERRT (execution extended RRT), introduces two novel extensions of previous RRT work, the waypoint cache and adaptive cost search, which improve replanning efficiency and the quality of generated paths. ERRT is successfully applied to a multi-robot system. Results demonstrate that ERRT is improves efficiency and performs competitively with existing heuristic and reactive real-time path planning approaches. ERRT has shown to offer a major step with great potential for path planning in challenging continuous, highly dynamic domains.

System and method for computer virus detection utilizing heuristic analysis

Abstract: A computer virus detection method includes compiling a list of heuristic events and a chronological order in which they occur, comparing the list of heuristic events and the chronological order with a defined list of heuristic events occurring in a defined chronological order and determining whether a computer virus is present based on a result of the comparing.

Wordlength optimization for linear digital signal processing

Abstract: This paper presents an approach to the wordlength allocation and optimization problem for linear digital signal processing systems implemented as custom parallel processing units. Two techniques are proposed, one which guarantees an optimum set of wordlengths for each internal variable, and one which is a heuristic approach. Both techniques allow the user to tradeoff implementation area for arithmetic error at system outputs. Optimality (with respect to the area and error estimates) is guaranteed through modeling as a mixed integer linear program. It is demonstrated that the proposed heuristic leads to area improvements of 6% to 45% combined with speed increases compared to the optimum uniform wordlength design. In addition, the heuristic reaches within 0.7% of the optimum multiple wordlength area over a range of benchmark problems.

Efficient synthesis of networks on chip

Abstract: We propose an efficient heuristic for the constraint-driven communication synthesis (CDCS) of on-chip communication networks. The complexity of the synthesis problems comes from the number of constraints that have to be considered. We propose to cluster constraints to reduce the number that needs to be considered by the optimization algorithm. Then a quadratic programming approach is used to solve the communication synthesis problem with the clustered constraints. We provide an analytical model that justifies our choice of the clustering cost function and we discuss a set of experiments showing the effectiveness of the overall approach with respect to the exact algorithm.

A study of waveband switching with multilayer multigranular optical cross-connects

Abstract: Waveband switching (WBS) has attracted attention from the optical networking industry for its practical importance in reducing port count, the associated control complexity, and cost of optical cross-connects (OXCs). However, WBS-related problems of theoretical interest have not been addressed thoroughly by the research community and many issues are still wide open. In particular, WBS is different from wavelength routing and, thus, techniques developed for wavelength-routed networks (including for example, those for traffic grooming) cannot be directly applied to effectively address WBS-related problems. In this paper, we first develop an integer linear programming (ILP) model, which for a given set of lightpath requests, determines the routes and assigns wavelengths to the lightpaths so as to minimize the number of ports needed. Since the optimal WBS problem of minimizing the port count in WBS networks contains an instance of routing and wavelength assignment (RWA), which is NP-complete, we adopt a powerful waveband assignment strategy and develop an efficient heuristic algorithm called balanced path routing with heavy-traffic first waveband assignment (BPHT). Both the ILP and the heuristic algorithm can handle the case with multiple fibers per link. We conduct a comprehensive evaluation of the benefits of WBS through detailed analysis and simulations. For small networks, our results indicate that the performance of the BPHT heuristic is close to that achievable by using the ILP model and, hence verifying its near-optimality. We show that for larger networks, BPHT can perform better than its variation called balanced traffic routing with maximum-hop first waveband assignment and much better than another heuristic based on optimal (but waveband oblivious) RWA that minimizes wavelength resources. We also show that WBS using BPHT is even more beneficial in multifiber networks than in single-fiber networks in terms of reducing the port count. Our analytical and simulation results provide valuable insights into the effect of wavelength band granularity, as well as the tradeoffs between the wavelength-hop and the port count required in WBS networks.

Optimal Time Transfer in Bus Transit Route Network Design using a Genetic Algorithm

Abstract: In this paper we propose a new model showing how genetic algorithms can be manipulated to help optimize bus transit routing design, incorporating unique service frequency settings for each route. The main lesson is in the power that can be given to heuristic methods if problem content is exploited appropriately. In this example, seven proposed genetic operators are designed for this specific problem to facilitate the search within a reasonable amount of time. In addition, headway coordination is applied by the ranking of transfer demands at the transfer terminals. The model is applied on a benchmark network to test its efficiency, and performance results are presented. It is shown that the proposed model is more efficient than the binary-coded genetic algorithm benchmark, in which problem content cannot be utilized.

Evolutionary multiobjective optimization for base station transmitter placement with frequency assignment

Abstract: We propose a new solution to the problem of positioning base station transmitters of a mobile phone network and assigning frequencies to the transmitters, both in an optimal way. Since an exact solution cannot be expected to run in polynomial time for all interesting versions of this problem (they are all NP-hard), our algorithm follows a heuristic approach based on the evolutionary paradigm. For this evolution to be efficient, i.e., goal-oriented and sufficiently random at the same time, problem-specific knowledge is embedded in the operators. The problem requires both the minimization of the cost and of the channel interference. We examine and compare two standard multiobjective techniques and a new algorithm - the steady-state evolutionary algorithm with Pareto tournaments. One major finding of the empirical investigation is a strong influence of the choice of the multiobjective selection method on the utility of the problem-specific recombination leading to a significant difference in the solution quality.

The Capacitated Lot-Sizing Problem with Linked Lot Sizes

Abstract: In this paper a new mixed integer programming (MIP) model formulation and its incorporation into a time-oriented decomposition heuristic for the capacitated lot-sizing problem with linked lot sizes (CLSPL) is proposed. The solution approach is based on an extended model formulation and valid inequalities to yield a tight formulation. Extensive computational tests prove the capability of this approach and show a superior solution quality with respect to other solution algorithms published so far.

A branch-and-cut algorithm for the single commodity uncapacitated fixed charge network flow problem

Abstract: We present a branch-and-cut algorithm to solve the single-commodity, uncapacitated, fixed-charge network flow problem, which includes the Steiner tree problem, uncapacitated lot-sizing problems, and the fixed-charge transportation problem as special cases. The cuts used are simple dicut inequalities and their variants. A crucial problem when separating these inequalities is to find the right cut set on which to generate the inequalities. The prototype branch-and-cut system, bc-nd, includes a separation heuristic for the dicut inequalities and problem-specific primal heuristics, branching, and pruning rules. Computational results show that bc-nd is competitive compared to a variety of special purpose algorithms for problems with explicit flow costs. We also examine how general purpose MIP systems perform on such problems when provided with formulations that have been tightened a priori with dicut inequalities.

A multi-resolution ICP with heuristic closest point search for fast and robust 3D registration of range images

Abstract: The iterative closest point (ICP) algorithm is widely used for the registration of 3D geometric data. One of the main drawbacks of the algorithm is its quadratic time complexity O(N/sup 2/) with the number of points N. Consequently, several methods have been proposed to accelerate the process. We present a new solution for the speeding up of the ICP algorithm and special care is taken to avoid any tradeoff with the quality of the registration. The proposed solution combines a coarse to fine multiresolution approach with the neighbor search algorithm. The multiresolution approach permits to successively improve the registration using finer levels of representation and the neighbor search algorithm speeds up the closest point search by using a heuristic approach. Both multiresolution scheme and neighbor search algorithm main features are presented. Confirming the success of the proposed solution, typical results show that this combination permits to create a very fast ICP algorithm, with a closest point search complexity of O(N), while preserving the matching quality.

Online response time optimization of Apache web server

Abstract: Properly optimizing the setting of configuration parameters can greatly improve performance, especially in the presence of changing workloads. This paper explores approaches to online optimization of the Apache web server, focusing on the MaxClients parameter (which controls the maximum number of workers). Using both empirical and analytic techniques, we show that MaxClients has a concave upward effect on response time and hence hill climbing techniques can be used to find the optimal value of MaxClients. We investigate two optimizers that employ hill climbing--one based on Newton's Method and the second based on fuzzy control. A third technique is a heuristic that exploits relationships between bottleneck utilizations and response time minimization. In all cases, online optimization reduces response times by a factor of 10 or more compared to using a static, default value. The trade-offs between the online schemes are as follows. Newton's method is well known but does not produce consistent results for highly variable data such as response times. Fuzzy control is more robust, but converges slowly. The heuristic works well in our prototype system, but it may be difficult to generalize because it requires knowledge of bottleneck resources and an ability to measure their utilizations.

An Approximation Scheme for Stochastic Integer Programs Arising in Capacity Expansion

Abstract: Planning for capacity expansion forms a crucial part of the strategic-level decision making in many applications. Consequently, quantitative models for economic capacity expansion planning have been the subject of intense research. However, much of the work in this area has been restricted to linear cost models and/or limited degree of uncertainty to make the problems analytically tractable. This paper addresses a stochastic capacity expansion problem where the economies-of-scale in expansion costs are handled via fixed-charge cost functions, and forecast uncertainties in the problem parameters are explicitly considered by specifying a set of scenarios. The resulting formulation is a multistage stochastic integer program. We develop a fast, linear-programming-based, approximation scheme that exploits the decomposable structure and is guaranteed to produce feasible solutions for this problem. Through a probabilistic analysis, we prove that the optimality gap of the heuristic solution almost surely vanishes asymptotically as the problem size increases.

Evacuation planning: a capacity constrained routing approach

Abstract: Evacuation planning is critical for applications such as disaster management and homeland defense preparation. Efficient tools are needed to produce evacuation plans to evacuate populations to safety in the event of catastrophes, natural disasters, and terrorist attacks. Current optimal methods suffer from computational complexity and may not scale up to large transportation networks. Current naive heuristic methods do not consider the capacity constraints of the evacuation network and may not produce feasible evacuation plans. In this paper, we model capacity as a time series and use a capacity constrained heuristic routing approach to solve the evacuation planning problem. We propose two heuristic algorithms, namely Single-Route Capacity Constrained Planner and Multiple-Route Capacity Constrained Planner to incorporate capacity constraints of the routes. Experiments on a real building dataset show that our proposed algorithms can produce close-to-optimal solution, which has total evacuation time within 10 percent longer than optimal solution, and also reduce the computational cost to only half of the optimal algorithm. The experiments also show that our algorithms are scalable with respect to the number of evacuees.

Robust optimization of OSPF/IS-IS weights

Abstract: In this paper, we adapt the heuristic of Fortz and Thorup for optimizing the weights of Shortest Path First protocols suchas Open Shortest Path First (OSPF) or Intermediate System-Intermediate System (IS-IS), in order to take into account failurescenarios.More precisely, we want to find a set of weights that is robust to all single link failures. A direct application of the originalheuristic, evaluating all the link failures, is too time consuming for realistic networks, so we developed a method based on acritical set of scenarios aimed to be representative of the whole set of scenarios. This allows us to make the problem manageableand achieve very robust solutions.

A simple heuristic for solving small fixed-charge transportation problems

Abstract: The fixed-charge transportation problem (FCTP) is an extension of the classical transportation problem in which a fixed cost is incurred, independent of the amount transported, along with a variable cost that is proportional to the amount shipped The introduction of fixed costs in addition to variable costs results in the objective function being a step function Therefore, fixed-charge problems are usually solved using sophisticated analytical or computer software This paper deviates from that approach It presents a simple heuristic algorithm for the solution of small fixed-charge problems We present numerical examples to illustrate applications of the proposed method

Wavelength converter placement under different RWA algorithms in wavelength-routed all-optical networks

Abstract: Sparse wavelength conversion and appropriate routing and wavelength assignment (RWA) algorithms are the two key factors in improving the blocking performance in wavelength-routed all-optical networks. It has been shown that the optimal placement of a limited number of wavelength converters in an arbitrary mesh network is an NP-complete problem. There have been various heuristic algorithms proposed in the literature, in which most of them assume that a static routing and random-wavelength assignment RWA algorithm is employed. However, the existing work shows that fixed-alternate routing and dynamic routing RWA algorithms can achieve much better blocking performance. Our study further demonstrates that the wavelength converter placement and RWA algorithms are closely related in the sense that a well-designed wavelength converter placement mechanism for a particular RWA algorithm might not work well with a different RWA algorithm. Therefore, the wavelength converter placement and the RWA have to be considered jointly. The objective of this paper is to investigate the wavelength converter placement problem under the fixed-alternate routing (FAR) algorithm and least-loaded routing (LLR) algorithm. Under the FAR algorithm, we propose a heuristic algorithm called minimum blocking probability first for wavelength converter placement. Under the LLR algorithm, we propose another heuristic algorithm called weighted maximum segment length. The objective of the converter placement algorithms is to minimize the overall blocking probability. Extensive simulation studies have been carried out over three typical mesh networks, including the 14-node NSFNET, 19-node EON, and 38-node CTNET. We observe that the proposed algorithms not only outperform existing wavelength converter placement algorithms by a large margin, but they also can achieve almost the same performance compared with full wavelength conversion under the same RWA algorithm.

A study of greedy, local search, and ant colony optimization approaches for car sequencing problems

Abstract: This paper describes and compares several heuristic approaches for the car sequencing problem. We first study greedy heuristics, and show that dynamic ones clearly outperform their static counterparts. We then describe local search and ant colony optimization (ACO) approaches, that both integrate greedy heuristics, and experimentally compare them on benchmark instances. ACO yields the best solution quality for smaller time limits, and it is comparable to local search for larger limits. Our best algorithms proved one instance being feasible, for which it was formerly unknown whether it is satisfiable or not.