Showing papers on "Heuristic (computer science) published in 2011"

A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems

Abstract: Artificial Bee Colony (ABC) algorithm was firstly proposed for unconstrained optimization problems on where that ABC algorithm showed superior performance. This paper describes a modified ABC algorithm for constrained optimization problems and compares the performance of the modified ABC algorithm against those of state-of-the-art algorithms for a set of constrained test problems. For constraint handling, ABC algorithm uses Deb's rules consisting of three simple heuristic rules and a probabilistic selection scheme for feasible solutions based on their fitness values and infeasible solutions based on their violation values. ABC algorithm is tested on thirteen well-known test problems and the results obtained are compared to those of the state-of-the-art algorithms and discussed. Moreover, a statistical parameter analysis of the modified ABC algorithm is conducted and appropriate values for each control parameter are obtained using analysis of the variance (ANOVA) and analysis of mean (ANOM) statistics.

Base Station Operation and User Association Mechanisms for Energy-Delay Tradeoffs in Green Cellular Networks

Abstract: Energy-efficiency, one of the major design goals in wireless cellular networks, has received much attention lately, due to increased awareness of environmental and economic issues for network operators. In this paper, we develop a theoretical framework for BS energy saving that encompasses dynamic BS operation and the related problem of user association together. Specifically, we formulate a total cost minimization that allows for a flexible tradeoff between flow-level performance and energy consumption. For the user association problem, we propose an optimal energy-efficient user association policy and further present a distributed implementation with provable convergence. For the BS operation problem (i.e., BS switching on/off), which is a challenging combinatorial problem, we propose simple greedy-on and greedy-off algorithms that are inspired by the mathematical background of submodularity maximization problem. Moreover, we propose other heuristic algorithms based on the distances between BSs or the utilizations of BSs that do not impose any additional signaling overhead and thus are easy to implement in practice. Extensive simulations under various practical configurations demonstrate that the proposed user association and BS operation algorithms can significantly reduce energy consumption.

Performance, accuracy, and Web server for evolutionary placement of short sequence reads under maximum likelihood.

Abstract: We present an evolutionary placement algorithm (EPA) and a Web server for the rapid assignment of sequence fragments (short reads) to edges of a given phylogenetic tree under the maximum-likelihood model. The accuracy of the algorithm is evaluated on several real-world data sets and compared with placement by pair-wise sequence comparison, using edit distances and BLAST. We introduce a slow and accurate as well as a fast and less accurate placement algorithm. For the slow algorithm, we develop additional heuristic techniques that yield almost the same run times as the fast version with only a small loss of accuracy. When those additional heuristics are employed, the run time of the more accurate algorithm is comparable with that of a simple BLAST search for data sets with a high number of short query sequences. Moreover, the accuracy of the EPA is significantly higher, in particular when the sample of taxa in the reference topology is sparse or inadequate. Our algorithm, which has been integrated into RAxML, therefore provides an equally fast but more accurate alternative to BLAST for tree-based inference of the evolutionary origin and composition of short sequence reads. We are also actively developing a Web server that offers a freely available service for computing read placements on trees using the EPA.

Covering Models and Optimization Techniques for Emergency Response Facility Location and Planning: A Review

Abstract: With emergencies being, unfortunately, part of our lives, it is crucial to efficiently plan and allocate emergency response facilities that deliver effective and timely relief to people most in need. Emergency Medical Services (EMS) allocation problems deal with locating EMS facilities among potential sites to provide efficient and effective services over a wide area with spatially distributed demands. It is often problematic due to the intrinsic complexity of these problems. This paper reviews covering models and optimization techniques for emergency response facility location and planning in the literature from the past few decades, while emphasizing recent developments. We introduce several typical covering models and their extensions ordered from simple to complex, including Location Set Covering Problem (LSCP), Maximal Covering Location Problem (MCLP), Double Standard Model (DSM), Maximum Expected Covering Location Problem (MEXCLP), and Maximum Availability Location Problem (MALP) models. In addition, recent developments on hypercube queuing models, dynamic allocation models, gradual covering models, and cooperative covering models are also presented in this paper. The corresponding optimization techniques to solve these models, including heuristic algorithms, simulation, and exact methods, are summarized.

Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming

Abstract: The heuristic k-means algorithm, widely used for cluster analysis, does not guarantee optimality. We developed a dynamic programming algorithm for optimal one-dimensional clustering. The algorithm is implemented as an R package called Ckmeans.1d.dp. We demonstrate its advantage in optimality and runtime over the standard iterative k-means algorithm.

Improved total variation-based CT image reconstruction applied to clinical data

Abstract: In computed tomography there are different situations where reconstruction has to be performed with limited raw data. In the past few years it has been shown that algorithms which are based on compressed sensing theory are able to handle incomplete datasets quite well. As a cost function these algorithms use the l(1)-norm of the image after it has been transformed by a sparsifying transformation. This yields to an inequality-constrained convex optimization problem. Due to the large size of the optimization problem some heuristic optimization algorithms have been proposed in the past few years. The most popular way is optimizing the raw data and sparsity cost functions separately in an alternating manner. In this paper we will follow this strategy and present a new method to adapt these optimization steps. Compared to existing methods which perform similarly, the proposed method needs no a priori knowledge about the raw data consistency. It is ensured that the algorithm converges to the lowest possible value of the raw data cost function, while holding the sparsity constraint at a low value. This is achieved by transferring the step-size determination of both optimization procedures into the raw data domain, where they are adapted to each other. To evaluate the algorithm, we process measured clinical datasets. To cover a wide field of possible applications, we focus on the problems of angular undersampling, data lost due to metal implants, limited view angle tomography and interior tomography. In all cases the presented method reaches convergence within less than 25 iteration steps, while using a constant set of algorithm control parameters. The image artifacts caused by incomplete raw data are mostly removed without introducing new effects like staircasing. All scenarios are compared to an existing implementation of the ASD-POCS algorithm, which realizes the step-size adaption in a different way. Additional prior information as proposed by the PICCS algorithm can be incorporated easily into the optimization process.

Routing and Spectrum Assignment in Spectrum Sliced Elastic Optical Path Network

Abstract: A spectrum-sliced elastic optical path network (SLICE) architecture has been recently proposed as an efficient solution for a flexible bandwidth allocation in optical networks In SLICE, the problem of Routing and Spectrum Assignment (RSA) emerges In this letter, we both formulate RSA as an Integer Linear Programming (ILP) problem and propose an effective heuristic to be used if the solution of ILP is not attainable

ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization

Abstract: Heuristic based computational algorithms are densely being used in many different fields due to their advantages When investigated carefully, chemical reactions possess efficient objects, states, process, and events that can be designed as a computational method en bloc In this study, a novel computational method, which is more robust and have less parameters than that of used in the literature, is intended to be developed inspiring from types and occurring of chemical reactions The proposed method is named as Artificial Chemical Reaction Optimization Algorithm, ACROA Applications to multiple-sequence alignment, data mining, and benchmark functions have been performed so as to put forward the performance of developed computational method

Tramp ship routing and scheduling with speed optimization

Abstract: Tramp shipping companies are committed to transport a set of contracted cargoes and try to derive additional revenue from carrying optional spot cargoes. Traditionally, models for ship routing and scheduling problems are based on fixed speed and a given fuel consumption rate for each ship. However, in real life a ship’s speed is variable within an interval, and fuel consumption per time unit can be approximated by a cubic function of speed. Here we present the tramp ship routing and scheduling problem with speed optimization, where speed on each sailing leg is introduced as a decision variable. We present a multi-start local search heuristic to solve this problem. To evaluate each move in the local search we have to determine the optimal speed for each sailing leg of a given ship route. To do this we propose two different algorithms. Extensive computational results show that the solution method solves problems of realistic size and that taking speed into consideration in tramp ship routing and scheduling significantly improves the solutions.

Genetic Algorithms: Concepts, Design for Optimization of Process Controllers

Abstract: Genetic Algorithm is a search heuristic that mimics the process of evaluation. Genetic Algorithms can be applied to process controllers for their optimization using natural operators. This paper discusses the concept and design procedure of Genetic Algorithm as an optimization tool. Further, this paper explores the well established methodologies of the literature to realize the workability and applicability of genetic algorithms for process control applications. Genetic Algorithms are applied to direct torque control of induction motor drive, speed control of gas turbine, speed control of DC servo motor for the optimization of control parameters in this work. The simulations were carried out in simulink package of MATLAB. The simulation results show better optimization of hybrid genetic algorithm controllers than fuzzy standalone and conventional controllers.

Integrated Methods for Optimization

Abstract: The first edition of Integrated Methods for Optimization was published in January 2007 Because the book covers a rapidly developing field, the time is right for a second edition The book provides a unified treatment of optimization methods It brings ideas from mathematical programming (MP), constraint programming (CP), and global optimization (GO)into a single volume There is no reason these must be learned as separate fields, as they normally are, and there are three reasons they should be studied together (1) There is much in common among them intellectually, and to a large degree they can be understood as special cases of a single underlying solution technology (2) A growing literature reports how they can be profitably integrated to formulate and solve a wide range of problems (3) Several software packages now incorporate techniques from two or more of these fields The book provides a unique resource for graduate students and practitioners who want a well-rounded background in optimization methods within a single course of study Engineering students are a particularly large potential audience, because engineering optimization problems often benefit from a combined approachparticularly where design, scheduling, or logistics are involved The text is also of value to those studying operations research, because their educational programs rarely cover CP, and to those studying computer science and artificial intelligence (AI), because their curricula typically omit MP and GO The text is also useful for practitioners in any of these areas who want to learn about another, because it provides a more concise and accessible treatment than other texts The book can cover so wide a range of material because it focuses on ideas that arerelevant to the methods used in general-purpose optimization and constraint solvers The book focuses on ideas behind the methods that have proved useful in general-purpose optimization and constraint solvers, as well as integrated solvers of the present and foreseeable future The second edition updates results in this area and includes several major new topics: Background material in linear, nonlinear, and dynamic programmingNetwork flow theory, due to its importance in filtering algorithmsA chapter on generalized duality theory that more explicitly develops a unifying primal-dual algorithmic structure for optimization methodsAn extensive survey of search methods from both MP and AI, using the primal-dual framework as an organizing principleCoverage of several additional global constraints used in CP solversThe book continues to focus on exact as opposed to heuristic methods It is possible to bring heuristic methods into the unifying scheme described in the book, and the new edition will retain the brief discussion of how this might be done

Simulated annealing based influence maximization in social networks

Abstract: The problem of influence maximization, i.e., mining top-k influential nodes from a social network such that the spread of influence in the network is maximized, is NP-hard. Most of the existing algorithms for the problem are based on greedy algorithm. Although greedy algorithm can achieve a good approximation, it is computational expensive. In this paper, we propose a totally different approach based on Simulated Annealing(SA) for the influence maximization problem. This is the first SA based algorithm for the problem. Additionally, we propose two heuristic methods to accelerate the convergence process of SA, and a new method of computing influence to speed up the proposed algorithm. Experimental results on four real networks show that the proposed algorithms run faster than the state-of-the-art greedy algorithm by 2-3 orders of magnitude while being able to improve the accuracy of greedy algorithm.

Optimal Generator Start-Up Strategy for Bulk Power System Restoration

Abstract: During system restoration, it is critical to utilize the available black-start (BS) units to provide cranking power to non-black-start (NBS) units in such a way that the overall system generation capability will be maximized. The corresponding optimization problem is combinatorial with complex practical constraints that can vary with time. This paper provides a new formulation of generator start-up sequencing as a mixed integer linear programming (MILP) problem. The linear formulation leads to an optimal solution to this important problem that clearly outperforms heuristic or enumerative techniques in quality of solutions or computational speed. The proposed generator start-up strategy is intended to provide an initial starting sequence of all BS or NBS units. The method can provide updates on the system MW generation capability as the restoration process progresses. The IEEE 39-Bus system, American Electric Power (AEP), and Entergy test cases are used for validation of the generation capability optimization. Simulation results demonstrate that the proposed MILP-based generator start-up sequencing algorithm is highly efficient.

An iterated greedy algorithm for the flowshop scheduling problem with blocking

Abstract: This paper proposes an iterated greedy algorithm for solving the blocking flowshop scheduling problem for makespan minimization. Moreover, it presents an improved NEH-based heuristic, which is used as the initial solution procedure for the iterated greedy algorithm. The effectiveness of both procedures was tested on some of Taillard’s benchmark instances that are considered to be blocking flowshop instances. The experimental evaluation showed the efficiency of the proposed algorithm, in spite of its simple structure, in comparison with a state-of-the-art algorithm. In addition, new best solutions for Taillard’s instances are reported for this problem, which can be used as a basis of comparison in future studies.

A branch-and-cut-and-price algorithm for the multi-depot heterogeneous vehicle routing problem with time windows

Abstract: We present a branch-and-cut-and-price algorithm for the exact solution of a variation of the vehicle routing problem with time windows in which the transportation fleet is made by vehicles with different capacities and fixed costs, based at different depots. We illustrate different pricing and cutting techniques and we present an experimental evaluation of their combinations. Computational results are reported on the use of the algorithm both for exact optimization and as a heuristic method.

The Linear Ordering Problem: Exact and Heuristic Methods in Combinatorial Optimization

Abstract: Faced with the challenge of solving the hard optimization problems that abound in the real world, existing methods often encounter great difficulties. Important applications in business, engineering or economics cannot be tackled by the techniques that have formed the predominant focus of academic research throughout the past three decades. Exact and heuristic approaches are dramatically changing our ability to solve problems of practical significance and are extending the frontier of problems that can be handled effectively. This monograph details state-of-the-art optimization methods, both exact and heuristic, for the LOP. The authors employ the LOP to illustrate contemporary optimization technologies as well as how to design successful implementations of exact and heuristic procedures. Therefore, they do not limit the scope of this book to the LOP, but on the contrary, provide the reader with the background and practical strategies in optimization to tackle different combinatorial problems.

An ant colony optimization model: The period vehicle routing problem with time windows

Abstract: This paper proposes an improved ant colony optimization (IACO) to solve period vehicle routing problem with time windows (PVRPTW), in which the planning period is extended to several days and each customer must be served within a specified time window. Multi-dimension pheromone matrix is used to accumulate heuristic information on different days. Two-crossover operations are introduced to improve the performance of the algorithm. The effectiveness of IACO is evaluated using a set of well-known benchmarks. Some of the results are better than the best-known solutions. Results also show the IACO seems to be a powerful tool for PVRPTW.

A QoS-Aware Power Optimization Scheme in OFDMA Systems with Integrated Device-to-Device (D2D) Communications

Abstract: This paper proposes a power optimization scheme with joint resource allocation (i.e. subcarrier and bit allocation) and mode selection in an OFDMA system with integrated D2D communications. Through the proper control of the base station (BS), users can communicate with each other either directly or via the BSs as in traditional cellular networks. Particularly, an optimization problem is formulated to minimize total downlink transmission power constrained by users' QoS demands; while a heuristic scheme exploiting joint subcarrier allocation, adaptive modulation and mode selection is contrived to solve the problem. Simulation results show that our proposed scheme may not only conserve total downlink transmission power effectively, but also save overall power consumption of BSs significantly, compared with existing algorithms used in traditional OFDMA systems.

Transit-network design methodology for actual-size road networks

Abstract: The main purpose of this study is to design a transit network of routes for handling actual-size road networks. This transit-network design problem is known to be complex and cumbersome. Thus, a heuristic methodology is proposed, taking into account the major concerns of transit authorities such as budget constraints, level-of-service standards and the attractiveness of the transit routes. In addition, this approach considers other important aspects of the problem including categorization of stops, multiclass of transit vehicles, hierarchy planning, system capacity (which has been largely ignored in past studies) and the integration between route-design and frequency-setting analyses. The process developed starts with the construction of a set of potential stops using a clustering concept. Then, by the use of Newton gravity theory and a special shortest-path procedure, a set of candidate routes is formed, categorized by hierarchy (mass, feeder, local routes). In the last step of the process a metaheuristic search engine is launched over the candidate routes, incorporating budgetary constraints, until a good solution is found. The algorithm was tested on the actual-size transit network of the city of Winnipeg; the results show that under the same conditions (budget and constraints) the proposed set of routes resulted in a reduction of 14% of total travel time compared to the existing transit network. In addition the methodology developed is compared favorably with other studies using the transit network of Mandl benchmark. The generality of the methodology was tested on the recent real dataset (pertaining to the year 2010) of the larger city of Chicago, in which a more efficient and optimized scheme was proposed for the existing rail system.

BAR: An Efficient Data Locality Driven Task Scheduling Algorithm for Cloud Computing

Abstract: Large scale data processing is increasingly common in cloud computing systems like MapReduce, Hadoop, and Dryad in recent years. In these systems, files are split into many small blocks and all blocks are replicated over several servers. To process files efficiently, each job is divided into many tasks and each task is allocated to a server to deals with a file block. Because network bandwidth is a scarce resource in these systems, enhancing task data locality(placing tasks on servers that contain their input blocks) is crucial for the job completion time. Although there have been many approaches on improving data locality, most of them either are greedy and ignore global optimization, or suffer from high computation complexity. To address these problems, we propose a heuristic task scheduling algorithm called Balance-Reduce(BAR), in which an initial task allocation will be produced at first, then the job completion time can be reduced gradually by tuning the initial task allocation. By taking a global view, BAR can adjust data locality dynamically according to network state and cluster workload. The simulation results show that BAR is able to deal with large problem instances in a few seconds and outperforms previous related algorithms in term of the job completion time.

Fire Fly Based Feature Selection Approach

Abstract: Irrelevant, noisy and high dimensional data, containing large number of features, degrades the performance of data mining and machine learning tasks. One of the methods used in data mining to reduce the dimensionality of data is feature selection. Feature selection methods select a subset of features that represents original features in problem domain with high accuracy. Various methods have been proposed that utilize heuristic or nature inspired strategies along with Rough Set Theory (RST) to find these subsets. However these methods either consume more time to find subset or compromise with optimality. The paper presents a new feature selection approach that combines the RST with nature inspired ‘firefly’ algorithm. The algorithm simulates the attraction system of real fireflies that guides the feature selection procedure. The experimental result proves that the proposed algorithm scores over other feature selection method in terms of time and optimality.

Optimization-Based Adaptive Large Neighborhood Search for the Production Routing Problem

Abstract: Operational problems arising in the planning of integrated supply chains have been increasingly studied in the past decade. Among these, the production routing problem (PRP) is a difficult problem that aims to jointly optimize production, inventory, distribution, and routing decisions in order to satisfy the dynamic demand of customers and minimize the overall system cost. This paper introduces an optimization-based adaptive large neighborhood search heuristic for the PRP. In this heuristic, binary variables representing setup and routing decisions are handled by an enumeration scheme and upper-level search operators, respectively, and continuous variables associated with production, inventory, and shipment quantities are set by solving a network flow subproblem. Extensive computational experiments have been performed on benchmark instances from the literature. The results show that our algorithm generally outperforms existing heuristics for the PRP and can produce high-quality solutions in short computin...