Fast Heuristic Algorithm Research Articles

A lightweight service placement approach for community network micro-clouds

Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. While Internet access is the most popular service, the provision of services of local interest within the network is enabled by the emerging technology of CN micro-clouds. By putting services closer to users, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of these services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, a “careful” placement of micro-clouds services over the network is required to optimize service performance. This paper proposes to leverage state information about the network to inform service placement decisions, and to do so through a fast heuristic algorithm, which is critical to quickly react to changing conditions. To evaluate its performance, we compare our heuristic with one based on random placement in Guifi.net, the biggest CN worldwide. Our experimental results show that our heuristic consistently outperforms random placement by 2x in bandwidth gain. We quantify the benefits of our heuristic on a real live video-streaming service, and demonstrate that video chunk losses decrease significantly, attaining a 37% decrease in the packet loss rate. Further, using a popular Web 2.0 service, we demonstrate that the client response times decrease up to an order of magnitude when using our heuristic. Since these improvements translate in the QoE (Quality of Experience) perceived by the user, our results are relevant for contributing to higher QoE, a crucial parameter for using services from volunteer-based systems and adapting CN micro-clouds as an eco-system for service deployment.

Relaxations and heuristics for the multiple non-linear separable knapsack problem

We consider the multiple non-linear knapsack problem with separable non-convex functions. The problem, which can be modeled as a (mixed) integer non-linear program, is extremely difficult to solve in practice. We present a fast heuristic algorithm, based on constructive techniques, surrogate relaxations, and local search improvements. Computational comparisons with exact and heuristic methods for general non-convex mixed integer non-linear programs show that the proposed approach provides good-quality solutions within small computing times.

Location‐aware targeted influence maximization in social networks

In this paper, we study the location‐aware targeted influence maximization problem in social networks, which finds a seed set to maximize the influence spread over the targeted users. In particular, we consider those users who have both topic and geographical preferences on promotion products as targeted users. To efficiently solve this problem, one challenge is how to find the targeted users and compute their preferences efficiently for given requests. To address this challenge, we devise a TR‐tree index structure, where each tree node stores users' topic and geographical preferences. By traversing the TR‐tree in depth‐first order, we can efficiently find the targeted users. Another challenge of the problem is to devise algorithms for efficient seeds selection. We solve this challenge from two complementary directions. In one direction, we adopt the maximum influence arborescence (MIA) model to approximate the influence spread, and propose two efficient approximation algorithms with approximation ratio, which prune some candidate seeds with small influences by precomputing users' initial influences offline and estimating the upper bound of their marginal influences online. In the other direction, we propose a fast heuristic algorithm to improve efficiency. Experiments conducted on real‐world data sets demonstrate the effectiveness and efficiency of our proposed algorithms.

A fast heuristic detection algorithm for visualizing structure of large community

With the increase in number of users, social networks data is growing more big and complex to examine mutual information between different objects. Different graph visualization algorithms are used to explore such a big and complex network data. Network graphs are naturally complex and can have overlapping contents. In this paper, a novel clustering based visualization algorithm is proposed to draw network graph with reduced visual complexity. The proposed algorithm neither comprises of any random element nor it requires any pre-determined number of communities. Because of its less computational time i.e. O(nlogn), it can be applied effectively on large scale networks. We tested our algorithm on thirteen different types and scales of real-world complex networks ranging from N=101 to N=106 vertices. The performance of the proposed algorithms is compared with six existing widely used graph clustering algorithms. The experimental results show superiority of our algorithm over existing algorithms in terms of execution speed, accuracy, and visualization.

Element Partition Trees For H-Refined Meshes to Optimize Direct Solver Performance. Part I: Dynamic Programming

Abstract We consider a class of two- and three-dimensional h-refined meshes generated by an adaptive finite element method. We introduce an element partition tree, which controls the execution of the multi-frontal solver algorithm over these refined grids. We propose and study algorithms with polynomial computational cost for the optimization of these element partition trees. The trees provide an ordering for the elimination of unknowns. The algorithms automatically optimize the element partition trees using extensions of dynamic programming. The construction of the trees by the dynamic programming approach is expensive. These generated trees cannot be used in practice, but rather utilized as a learning tool to propose fast heuristic algorithms. In this first part of our paper we focus on the dynamic programming approach, and draw a sketch of the heuristic algorithm. The second part will be devoted to a more detailed analysis of the heuristic algorithm extended for the case of hp-adaptive grids.

A fast heuristic optimization algorithm for measurement placement in distribution system state estimation

An accurate, real‐time estimation of the states of a power distribution system is highly desirable but hard to achieve because of the complexity of the network and the relative inefficiency of the measuring system. To increase the efficiency, this paper analyzes the mathematical relationship between the measurement errors and estimation errors of the state vector using the classic weighted least square method. Then a heuristic algorithm is proposed to improve the accuracy by optimizing the deployment of the real‐time measuring points, which is based on the deterministic factors of the measuring points/branches over the system state. The basic implementation starts with an initial measurement set and replaces the least important branches in the set with the most important branches outside the set using iterative optimization. The algorithm was tested in the IEEE 14‐bus and 33‐bus distribution systems and achieved 50% increase in accuracy at much lower computational cost compared with exhaustive search. Moreover, the proposed algorithm has also been compared with representative and widely used evolution algorithms such as particle swarm optimization and quantum‐behaved particle swarm optimization. This comparison shows that our method can achieve stable and comparable accuracy with a speed at least 10 times higher. The performance of our method can be even better with increasing network size. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Convergecast scheduling and cost optimization for industrial wireless sensor networks with multiple radio interfaces

Industrial wireless sensor networks have been widely deployed in many industrial systems. The main communication paradigm of such systems, known as convergecast, is to converge sensing data to a centralized manager. The rapid and reliable data convergecast is essential to the industrial production. Multiple radio interfaces on a network device and convergecast scheduling algorithms can effectively reduce convergecast delay. Existing works confine to the convergecast based on linear- and tree-based routing. Compared to the two routing schemes, graph routing is more reliable. Although the graph routing gains more popularity in industrial networks due to its better reliability, few works have addressed its temporality performance. On the other hand, the number of radio interfaces also impacts on the convergecast delay. In this paper, we present a holistic framework to solve how to use multiple radio interfaces to converge data. First, we propose a convergecast scheduling algorithm for industrial wireless sensor networks with multiple radio interfaces. Second, based on our proposed scheduling algorithm, we propose an optimal algorithm and a fast heuristic algorithm to minimize the number of radio interfaces under the temporality constraint of industrial production. Evaluations show that all our algorithms perform closely to the optimal solution.

Parallel Algorithms for Effective Correspondence Problem Solution in Computer Vision

We propose new parallel algorithms for correspondence problem solution in computer vision. We develop an industrial photogrammetric system that uses artificial retroreflective targets that are photometrically identical. Therefore, we cannot use traditional descriptor-based point matching methods, such as SIFT, SURF etc. Instead, we use epipolar geometry constraints for finding potential point correspondences between images. In this paper, we propose new effective graph-based algorithms for finding point correspondences across the whole set of images (in contrast to traditional methods that use 2-4 images for point matching). We give an exact problem solution via superclique and show that this approach cannot be used for real tasks due to computational complexity. We propose a new effective parallel algorithm that builds the graph from epipolar constraints, as well as a new fast parallel heuristic clique finding algorithm. We use an iterative scheme (with backprojection of the points, filtering of outliers and bundle adjustment of point coordinates and cameras’ positions) to obtain an exact correspondence problem solution. This scheme allows using heuristic clique finding algorithm at each iteration. The proposed architecture of the system offers a significant advantage in time. Newly proposed algorithms have been implemented in code; their performance has been estimated. We also investigate their impact on the effectiveness of the photogrammetric system that is currently under development and experimentally prove algorithms’ efficiency.

A Fast Heuristic Algorithm for Solving High-Density Subset-Sum Problems

A Fast Heuristic Algorithm for Solving High-Density Subset-Sum Problems

Design analysis of gas engine combined heat and power plants (CHP) for building and industry heat demand under varying price structures

Combined heat and power (CHP) plants based on gas engines feature overall efficiencies of 90%, response times of less than 2 min, tolerate multiple starts per day and can be deployed as decentralised generators. A decoupling heat storage device between the plant and the heat sink can improve operation flexibility, but increases investment costs. The cost-optimal sizing of plant and storage against time dependent electricity prices is a non-trivial optimisation problem. In this study, we investigate how the optimal design depends on various boundary conditions. We sweep residential and industrial heat demand profiles (5 kW–100 MW peak), electricity price levels and variance and fuel prices. We pair a linear plant model with heat sink and price combinations and use a fast heuristic algorithm to find power, storage size and operating pattern for maximised annual profit over 8760 h. Brake-even is reached with a surplus of 0.03–0.14 €/kWh on today's spot market price (fuel 0.08 €/kWh). Design results for plants ≥ 1 MW power are similar. Future optimal designs are up to 30% larger than today's and profits increase. The design is generally robust on expected price changes due to the flat optimum. The results provide a valuable basis for designing profitable plants today and in future.

Reconditioning: A Framework for Automatic Power Optimization of QDI Circuits

This paper introduces reconditioning: a novel systematic technique for reducing unnecessary power consumption of asynchronous gate-level netlists, which involves the optimal reordering of conditional communication and logic primitives. Our technique is applicable to asynchronous circuits with handshaking protocols that encode data and control together, in particular, quasi delay insensitive and 1-of- ${N}$ handshaking circuits. Both an optimal integer linear program (ILP) and a fast heuristic algorithm are presented. We show that our ILP is feasible for moderate size circuits and our heuristic algorithm scales to much larger circuits, completing in seconds on circuits with tens of thousands of gates. Our experimental results show power improvement highly depends on the structure of the circuit but can often be above 26% with typically less than 5% area overhead.

Heuristic approach for the economic optimisation of combined heat and power (CHP) plants: Operating strategy, heat storage and power

CHP plants produce power and heat in parallel and operate between power and heat consumers as well as prices for their supply. The profitability of a plant with given power depends on operating strategy and heat storage size. In this paper, we present a fast heuristic algorithm to determine the most profitable setting for given boundary conditions, as a substitute for the state-of-the-art approach based on mixed-integer linear programming (MILP). The presented heuristic algorithm is appropriate for thousands of plant calculations or real-time operation planning of single as well as interconnected plants. We used hourly resolved full-year data reflecting electricity market price and heat demand, which was simulated using a typical multi-family house configuration. The heuristic algorithm found the optimal operating strategy and heat storage size 34 times faster than the MILP solver. Additional optimisation runs of the operating pattern at various fixed heat storage sizes, resulted in equal solutions of both approaches. The heuristic algorithm found in this case the operating patterns around 200 times faster. Furthermore, the heuristic algorithm can be enhanced to find optimal CHP power, heat storage size and operating strategy in one process using characteristic curves for plant efficiencies and costs.

Tiers for peers: a practical algorithm for discovering hierarchy in weighted networks

Interactions in many real-world phenomena can be explained by a strong hierarchical structure. Typically, this structure or ranking is not known; instead we only have observed outcomes of the interactions, and the goal is to infer the hierarchy from these observations. Discovering a hierarchy in the context of directed networks can be formulated as follows: given a graph, partition vertices into levels such that, ideally, there are only edges from upper levels to lower levels. The ideal case can only happen if the graph is acyclic. Consequently, in practice we have to introduce a penalty function that penalizes edges violating the hierarchy. A practical variant for such penalty is agony, where each violating edge is penalized based on the severity of the violation. Hierarchy minimizing agony can be discovered in $O(m^2)$ time, and much faster in practice. In this paper we introduce several extensions to agony. We extend the definition for weighted graphs and allow a cardinality constraint that limits the number of levels. While, these are conceptually trivial extensions, current algorithms cannot handle them, nor they can be easily extended. We solve the problem by showing the connection to the capacitated circulation problem, and we demonstrate that we can compute the exact solution fast in practice for large datasets. We also introduce a provably fast heuristic algorithm that produces rankings with competitive scores. In addition, we show that we can compute agony in polynomial time for any convex penalty, and, to complete the picture, we show that minimizing hierarchy with any concave penalty is an NP-hard problem.

Classification of ligand molecules in PDB with graph match-based structural superposition.

The fast heuristic graph match algorithm for small molecules, COMPLIG, was improved by adding a structural superposition process to verify the atom-atom matching. The modified method was used to classify the small molecule ligands in the Protein Data Bank (PDB) by their three-dimensional structures, and 16,660 types of ligands in the PDB were classified into 7561 clusters. In contrast, a classification by a previous method (without structure superposition) generated 3371 clusters from the same ligand set. The characteristic feature in the current classification system is the increased number of singleton clusters, which contained only one ligand molecule in a cluster. Inspections of the singletons in the current classification system but not in the previous one implied that the major factors for the isolation were differences in chirality, cyclic conformations, separation of substructures, and bond length. Comparisons between current and previous classification systems revealed that the superposition-based classification was effective in clustering functionally related ligands, such as drugs targeted to specific biological processes, owing to the strictness of the atom-atom matching.

QoS-Aware Admission Control and Resource Allocation in Underlay Device-to-Device Spectrum-Sharing Networks

Device-to-device (D2D) communications underlaying a cellular infrastructure have been recognized as an important network-organization architecture in 5G networks. In these scenarios, existing works have explored the impacts of one or several factors among mode selection, admission control, partner assignment, and power allocation on the network performance. In this paper, we put forward an optimization framework that considers all of these coupled factors to investigate the spectrum sharing problem in D2D networks. In particular, we introduce an objective that combines the access rate and the network sum rate and then maximize it subject to users’ quality-of-service requirements and resource allocation constraints. Due to its mixed combination, we focus on designing cost-efficient and easy-to-implement algorithms instead of finding globally optimal but exponentially complex solutions. By decomposition, we first devise two novel mode selection criteria and an admission-prioritized partner assignment scheme and obtain closed-form solutions for both admission control and power allocation. Moreover, we present a simple but interesting geometric interpretation on the physical implication of admission conditions. To further reduce the computational cost, we also exploit the structure of the formulation to devise a much faster heuristic algorithm, which usually runs at an order of millisecond. Simulation results show the low computational complexity of the proposed algorithms and exhibit their superiority against other schemes in terms of the access rate and the sum rate.

Scheduling Variable Maintenances and Jobs on Unrelated Parallel Machines to Minimize the Total Completion Time

SYNOPTIC ABSTRACTWe investigate an unrelated parallel-machine scheduling problem in which each machine must perform a variable mandatory maintenance prior to a given deadline. A variable maintenance means that the duration of the maintenance depends on its starting time, i.e., the later the maintenance starts, the longer it takes to complete. The task is to schedule all the jobs and determine the starting times of each maintenance such that the total completion time is minimized. We show that the problem is strongly NP-hard. Furthermore, we provide a fast heuristic algorithm, followed by a numerical example.

Energy-aware traffic engineering in hybrid SDN/IP backbone networks

Software Defined Networking (SDN) can effectively improve the performance of traffic engineering and has promising application foreground in backbone networks. Therefore, new energy saving schemes must take SDN into account, which is extremely important considering the rapidly increasing energy consumption from Telecom and ISP networks. At the same time, the introduction of SDN in a current network must be incremental in most cases, for both technical and economic reasons. During this period, operators have to manage hybrid networks, where SDN and traditional protocols coexist. In this paper, we study the energy efficient traffic engineering problem in hybrid SDN/IP networks. We first formulate the mathematic optimization model considering SDN/IP hybrid routing mode. As the problem is NP-hard, we propose the fast heuristic algorithm named HEATE (Hybrid Energy-Aware Traffic Engineering). In our proposed HEATE algorithm, the IP routers perform the shortest path routing using the distribute OSPF link weight optimization. The SDNs perform the multi-path routing with traffic flow splitting by the global SDN controller. The HEATE algorithm finds the optimal setting of OSPF link weight and splitting ratio of SDNs. Thus traffic flow is aggregated onto partial links and the underutilized links can be turned off to save energy. By computer simulation results, we show that our algorithm has a significant improvement in energy efficiency in hybrid SDN/IP networks.

Min–max–min robust combinatorial optimization

The idea of k-adaptability in two-stage robust optimization is to calculate a fixed number k of second-stage policies here-and-now. After the actual scenario is revealed, the best of these policies is selected. This idea leads to a min---max---min problem. In this paper, we consider the case where no first stage variables exist and propose to use this approach to solve combinatorial optimization problems with uncertainty in the objective function. We investigate the complexity of this special case for convex uncertainty sets. We first show that the min---max---min problem is as easy as the underlying certain problem if k is greater than the number of variables and if we can optimize a linear function over the uncertainty set in polynomial time. We also provide an exact and practical oracle-based algorithm to solve the latter problem for any underlying combinatorial problem. On the other hand, we prove that the min---max---min problem is NP-hard for every fixed number k, even when the uncertainty set is a polyhedron, given by an inner description. For the case that k is smaller or equal to the number of variables, we finally propose a fast heuristic algorithm and evaluate its performance.

G-MAPSEQ – a new method for mapping reads to a reference genome

Abstract The problem of reads mapping to a reference genome is one of the most essential problems in modern computational biology. The most popular algorithms used to solve this problem are based on the Burrows-Wheeler transform and the FM-index. However, this causes some issues with highly mutated sequences due to a limited number of mutations allowed. G-MAPSEQ is a novel, hybrid algorithm combining two interesting methods: alignment-free sequence comparison and an ultra fast sequence alignment. The former is a fast heuristic algorithm which uses k-mer characteristics of nucleotide sequences to find potential mapping places. The latter is a very fast GPU implementation of sequence alignment used to verify the correctness of these mapping positions. The source code of G-MAPSEQ along with other bioinformatic software is available at: http://gpualign.cs.put.poznan.pl.

이산 프레셰 거리 척도를 이용한 궤적 유사도 고속계산 휴리스틱 알고리즘

A trajectory is the motion path of a moving object. The advances in IT have made it possible to collect an immeasurable amount of various type of trajectory data from a moving object using location detection devices like GPS. The trajectories of moving objects are widely used in many different fields of research, including the geographic information system (GIS) field. In the GIS field, several attempts have been made to automatically generate digital maps of roads by using the vehicle trajectory data. To achieve this goal, the method to cluster the trajectories on the same road is needed. Usually, the distance measure is used to calculate the distance between a pair of trajectories. However, the distance measure requires prolonged calculation time for a large amount of trajectories. In this paper, we presented a fast heuristic algorithm to distinguish whether the trajectories are in close distance or not using the discrete distance measure. This algorithm trades the accuracy of the resulting distance with decreased calculation time. By experiments, we showed that the algorithm could distinguish between the trajectory within 10 meters and the distant trajectory with 95% accuracy and, at worst, 65% of calculation reduction, as compared with the discrete distance.