Effective Heuristic Algorithm Research Articles

Fast and Accurate Algorithms for Mapping and Aligning Long Reads.

For DNA sequence analysis, we are facing challenging tasks such as the identification of structural variants, sequencing repetitive regions, and phasing of alleles. Those challenging tasks suffer from the short length of sequencing reads, where each read may cover less than 2 single nucleotide polymorphism (SNP), or less than two occurrences of a repeated region. It is believed that long reads can help to solve those challenging tasks. In this study, we have designed new algorithms for mapping long reads to reference genomes. We have also designed efficient and effective heuristic algorithms for local alignments of long reads against the corresponding segments of the reference genome. To design the new mapping algorithm, we formulate the problem as the longest common subsequence with distance constraints. The local alignment heuristic algorithm is based on the idea of recursive alignment of k-mers, where the size of k differs in each round. We have implemented all the algorithms in C++ and produce a software package named mapAlign. Experiments on real data sets showed that the newly proposed approach can generate better alignments in terms of both identity and alignment scores for both Nanopore and single molecule real time sequencing (SMRT) data sets. For human individuals of both Nanopore and SMRT data sets, the new method can successfully math/align 91.53% and 85.36% of letters from reads to identical letters on reference genomes, respectively. In comparison, the best known method can only align 88.44% and 79.08% letters of reads for Nanopore and SMRT data sets, respectively. Our method is also faster than the best known method.

A variable neighborhood search approach for the resource-constrained multi-project collaborative scheduling problem

Multi-mode and multi-skill project scheduling problems widely exist in the real-world industry. Many efforts have been devoted to the multi-mode project scheduling problem and the multi-skill project scheduling problem. However, the integrated multi-mode and multi-skill project scheduling problem was seldom considered. This paper studies the resource-constrained multi-project scheduling problem in the development of high-end equipment and develops an integrated multi-mode and multi-skill scheduling model with different employee abilities. Based on the characteristics of high-end equipment development, we propose several optimal properties and design an effective heuristic algorithm. Since the resource-constrained project scheduling problem is NP-hard, three neighborhoods for the problem are constructed and a Variable Neighborhood Search Algorithm (VNS) is developed to solve the problem in a reasonable time. Finally, computational experiments are carried out to validate the performance of the proposed algorithm.

Neighborhood decomposition-driven variable neighborhood search for capacitated clustering

The capacitated clustering problem (CCP) is a general model relevant for a variety of important applications in areas such as parallel computing and very large scale integration design. However, the problem is known to be NP-hard, and thus computationally challenging. In this work, we present an original and highly effective variable neighborhood search algorithm for the problem, which is characterized by its neighborhood decomposition technique and a probability-based diversification strategy. The proposed algorithm is assessed via extensive experiments on 110 benchmark instances commonly used in the literature. Computational results show that the algorithm significantly outperforms the existing state-of-the-art algorithms in the literature. This work advances the state-of-the-art of solving the capacitated clustering problem and can be useful for the related practical applications. The key feature of the algorithm, i.e., combining the neighborhood decomposition-driven local search with the perturbation, is of general interest and can help to design effective heuristic algorithms for other important clustering problems.

ОБ ИСПОЛЬЗОВАНИИ ЭВРИСТИЧЕСКИХ АЛГОРИТМОВ В ЗАДАЧАХ ОПТИМАЛЬНОГО ПАРАМЕТРИЧЕСКОГО СИНТЕЗА

The problem of optimal synthesis of engineering systems is considered, with due account for the ran-dom changes in their production and operational parameters. A number of ways and means of creating effective heuristic algorithms for multidimensional search optimization are proposed to solve the emerging problems. The paper focuses on the bat algorithm and the creation of its parallel modifica-tion.

Best-First Beam Search

Decoding for many NLP tasks requires an effective heuristic algorithm for approximating exact search because the problem of searching the full output space is often intractable, or impractical in many settings. The default algorithm for this job is beam search—a pruned version of breadth-first search. Quite surprisingly, beam search often returns better results than exact inference due to beneficial search bias for NLP tasks. In this work, we show that the standard implementation of beam search can be made up to 10x faster in practice. Our method assumes that the scoring function is monotonic in the sequence length, which allows us to safely prune hypotheses that cannot be in the final set of hypotheses early on. We devise effective monotonic approximations to popular nonmonontic scoring functions, including length normalization and mutual information decoding. Lastly, we propose a memory-reduced variant of best-first beam search, which has a similar beneficial search bias in terms of downstream performance, but runs in a fraction of the time.

Chaotic improved PICEA-g-based multi-objective optimization for workflow scheduling in cloud environment

Mapping of workflow tasks to computational resources in the cloud environment has engendered research interest in workflow scheduling. As workflow scheduling belongs to NP-complete problem, so building an optimum workflow scheduler with reasonable performance and computation speed is very challenging in the heterogeneous distributed environment of clouds. Many existing studies deal with cloud workflow scheduling as a single or bi-objective optimization problem without considering some important requirements of the users or the providers. Therefore, it is highly desirable to formulate scheduling of the workflow applications as a Multi-objective Optimization Problem (MOP) taking into account the requirements from the user and the service provider. For example, the cloud workflow scheduler might wish to consider user’s Quality of Service (QoS) objectives, such as makespan and cost, as well as provider’s objectives, such as energy efficiency over the Virtual Machines (VMs). In addition, early convergence in existing algorithms is a problem that increases the number of repetitions for reaching a global optimum. To overcome these drawbacks, in this paper, an enhanced multi-objective co-evolutionary algorithm, called ch-PICEA-g, is proposed as an effective heuristic algorithm, where the logistic and tent maps as two chaotic systems are applied in generating chaotic values to overcome the permute convergence in the initial population and the genetic operators. Also, an improved fitness function is applied to increase the performance of original PICEA-g. The functionality of the proposed algorithm is validated by extensive experiments. The obtained results indicate that this proposed algorithm outperforms its counterparts in terms of different performance metrics.

Incentive-Aware Recruitment of Intelligent Vehicles for Edge-Assisted Mobile Crowdsensing

Edge-assisted mobile crowdsensing is an emerging paradigm where mobile users collect, and share sensing data at the edge of networks. With the abundant on-board resources, and large movement patterns of intelligent vehicles, they have become candidates to sense up-to-date, and fine-grained information for large areas. The design of vehicle recruitment in edge-assisted mobile crowdsensing is challenging due to the selfishness, and the uneven distribution of vehicles, as well as the spatiotemporal constraints of vehicular crowdsensing applications. To deal with these challenges, this paper proposes an incentive-aware vehicle recruitment scheme for edge-assisted mobile crowdsensing. In particular, we first design an incentive mechanism to motivate cooperation among the edge server, and the intelligent vehicles, and apply the Nash bargaining theory to obtain the optimal cooperation decision. Furthermore, a practical, and efficient scheme is proposed to weigh the contribution of vehicles. Then, we formulate the participant recruitment as an optimization problem, and prove that it is NP-hard. To address this problem, an effective heuristic algorithm with a guaranteed approximation ratio is proposed, by leveraging the property in submodular optimization. Finally, we conduct extensive simulations, based on a real dataset, to validate the superiority of the proposed schemes.

Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates

Manufacturing industry reflects a country’s productivity level and occupies an important share in the national economy of developed countries in the world. Jobshop scheduling (JSS) model originates from modern manufacturing, in which a number of tasks are executed individually on a series of processors following their preset processing routes. This study addresses a JSS problem with the criterion of minimizing total quadratic completion time (TQCT), where each task is available at its own release date. Constructive heuristic and meta-heuristic algorithms are introduced to handle different scale instances as the problem is NP-hard. Given that the shortest-processing-time (SPT)-based heuristic and dense scheduling rule are effective for the TQCT criterion and the JSS problem, respectively, an innovative heuristic combining SPT and dense scheduling rule is put forward to provide feasible solutions for large-scale instances. A preemptive single-machine-based lower bound is designed to estimate the optimal schedule and reveal the performance of the heuristic. Differential evolution algorithm is a global search algorithm on the basis of population, which has the advantages of simple structure, strong robustness, fast convergence, and easy implementation. Therefore, a hybrid discrete differential evolution (HDDE) algorithm is presented to obtain near-optimal solutions for medium-scale instances, where multi-point insertion and a local search scheme enhance the quality of final solutions. The superiority of the HDDE algorithm is highlighted by contrast experiments with population-based meta-heuristics, i.e., ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). Average gaps 45.62, 63.38 and 188.46 between HDDE with ACO, PSO and GA, respectively, are demonstrated by the numerical results with benchmark data, which reveals the domination of the proposed HDDE algorithm.

A new iterated local search algorithm for the cyclic bandwidth problem

The Cyclic Bandwidth Problem is an important graph labeling problem with numerous applications. This work aims to advance the state-of-the-art of practically solving this computationally challenging problem. We present an effective heuristic algorithm based on the general iterated local search framework and integrating dedicated search components. Specifically, the algorithm relies on a simple, yet powerful local optimization procedure reinforced by two complementary perturbation strategies. The local optimization procedure discovers high-quality solutions in a particular search zone while the perturbation strategies help the search to escape local optimum traps and explore unvisited areas. We present intensive computational results on 113 benchmark instances from 8 different families, and show performances that are never achieved by current best algorithms in the literature.

Tradeoff Between Location Quality and Privacy in Crowdsensing: An Optimization Perspective

Crowdsensing enables a wide range of data collection, where the data are usually tagged with private locations. Protecting users' location privacy has been a central issue. The study of various location perturbation techniques, e.g., k-anonymity, for location privacy has received widespread attention. Despite the huge promise and considerable attention, provable good algorithms considering the tradeoff between location privacy and location information quality from the optimization perspective in crowdsensing are lacking in the literature. In this article, we study two related optimization problems from two different perspectives. The first problem is to minimize the location quality degradation caused by the protection of users' location privacy. We present an efficient optimal algorithm OLoQ for this problem. The second problem is to maximize the number of protected users, subject to a location quality degradation constraint. To satisfy the different requirements of the platform, we consider two cases for this problem: 1) overlapping and 2) nonoverlapping perturbations. For the former case, we give an efficient optimal algorithm OPUM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</sub> . For the latter case, we first prove its NP-hardness. We then design a (1-E)-approximation algorithm NPUM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> and a fast and effective heuristic algorithm HPUM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> . Extensive simulations demonstrate that OLoQ, OPUM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</sub> , and HPUM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> significantly outperform an existing algorithm.

WCA: A weighting local search for constrained combinatorial test optimization

Abstract Context Covering array generation (CAG) is the core task of Combinatorial interaction testing (CIT), which is widely used to discover interaction faults in real-world systems. Considering the universality, constrained covering array generation (CCAG) is more in line with the characteristics of applications, and has attracted a lot of researches in the past few years. Objective In CIT, a covering array (CA) with smaller size means lower cost of testing, particularly for the systems where the execution of a test suite is time consuming. As constraints between parameters are ubiquitous in real systems, this work is dedicated to more efficient algorithms for CCAG. Specifically, we aim to develop a heuristic search algorithm for CCAG, which allows generating CAs with smaller size in a limited time when compared with existing algorithms. Method We propose a weighting local search algorithm named WCA, which makes use of weights associated with the tuples and dynamically adjusts them during the search, helping the algorithm to avoid search stagnation. As far as we know, this is the first weighting local search for solving CCAG. Results We apply WCA to a wide range of benchmarks, including real-world ones and synthetic ones. The results show that WCA achieves a significant improvement over three state-of-the-art competitors in 2-way and 3-way CCAG, in terms of both effectiveness and efficiency. The importance of weighting is also reflected by the experimental comparison between WCA and its alternative algorithm without the weighting mechanism. Conclusion WCA is an effective heuristic algorithm for CCAG to obtain smaller CAs efficiently, and the weighting mechanism plays a crucial role.

A new unrelated parallel machine scheduling problem with tool changes to minimise the total energy consumption

The previous studies on scheduling problem with tool changes take processing time as the only reason for the tool wear, which is not accurate in the real manufacturing system. This paper takes processing speed and processing time into consideration simultaneously and proposes a new unrelated parallel machine scheduling problem (UPMSP) with tool changes caused by the tool wear, in which the energy consumption rate of the parallel machines is influenced by two factors: tool changes and corresponding processing speed. A new effective heuristic evolutionary algorithm (NHEA) is presented to solve the proposed UPMSP with objectives of optimising total energy consumption and makespan. For the NHEA, some effective operators such as target-searching operators are designed to accelerate the search efficiency and further exploit the solution space. A first fit decreasing algorithm is presented and incorporated into the NHEA to reduce the number of tool changes. The Taguchi method of Design of Experiments is used to obtain the best combination of key parameters of the NHEA. Extensive computational experiments are carried out to compare the NHEA with some well-known algorithms. The results validate that the proposed NHEA is able to obtain better Pareto solutions for UPMSP with tool changes.

Cache-Aware Network Coding-Based Scheduling for SVC Multicast in Wireless Networks with Hard Deadline Constraints

To provide multi-resolution video for heterogeneous users in wireless networks, Scalable Video Coding (SVC) encodes a video bit stream into one base layer and several enhancement layers, on the basis of their contribution in recovering the video correspondingly. However, a decoding dependency is introduced into these layers and artful operation is required for SVC streaming. Meanwhile, each SVC packet is associated with a hard time constraint so that users can recover their desired video quality timely. This paper focuses on the network coding assisted SVC multicast with hard deadline constraints. Specifically, we consider the cache-aware model where users are able to store all received packets and decode out the desired packets when sufficient packets are obtained. We analyze the effect of cached network coded packets on the SVC packets scheduling process and the video quality improvement, and value that effect through assigning proper weight for each packet. We also take multiple factors that affect quality of experience into consideration, such as channel condition, request’s deadline and user’s packets receiving status. Moreover, we adopt the instantly decodable network coding (IDNC) technique to schedule SVC packets. According to packets weighting on the vertices of a well-structured two-layered IDNC graph, an effective heuristic algorithm is proposed to schedule the most valuable SVC packets combinations within acceptable complexity. Extensive simulation results verify the effectiveness and the quality of experience enhancement accomplished by the proposed algorithm.

A Robust Network Design Problem Based on the Spatiotemporal Attributes of Activities

This study aims to investigate a robust network design problem to maximise the expected space-time accessibility, in which the origin-destination demand matrices are time-varying or unsynchronised under stochastic space-time scenarios. By defining the accessibility measure as the expected amount of accessible demand pairs, a linear 0–1 integer programming model is formulated to characterise the network design process. In order to find the near-optimal solutions for the problem of interest, an effective heuristic algorithm is proposed based on the framework of Lagrangian relaxation that decomposes the primal model into a series of tractable subproblems. Finally, the performance of the developed methods is demonstrated through numerical experiments in two networks with different scales. The results highlight the importance of considering robustness in transportation network design problems.

Maximal covering salesman problems with average traveling cost constrains

We study the maximal covering salesman problem with the average travelling cost constraints (MCSPATCC) where the objective is to find a subset of customers with their tour so that the number of covered demand points is maximised. This paper presents a mathematical model to select a profitable subset of demand points to be covered. We also propose an effective heuristic algorithm with three elimination methods to remove unprofitable demand points. The proposed algorithm is based on the genetic algorithm (GA) hybridised with different local search strategies to solve this problem. Parameters of the algorithm are analysed for calibration by the Taguchi method. Extensive computational experiments, on a set of standard problems, have indicated the effectiveness of our algorithm.

Privacy-preserved distinct content collection in human-assisted ubiquitous computing systems

Human beings with smart devices have dramatically facilitated the knowledge acquisition in ubiquitous computing systems. However, the collected contents also severely threat the security and privacy for individuals, as these contents achieve a fine-grained coverage of one’s behaviors and movements. Local differential privacy, as one of the widely accepted properties, enables individuals to contribute to data collection while remaining indistinguishable against adversaries. However, existing solutions and mechanisms fail to capture the contents with relatively low frequency, e.g., the temperature in local regions visited by very few people. Therefore, this work proposes a novel framework for distinct content estimation under local differential privacy. We propose a hash function and random response based framework for the estimation. The framework allows individuals in the ubiquitous computing system to participate with flexible bandwidth, and properly assigns the given bandwidth to improve estimated results. We prove the optimization problem of assignment in our framework to be NP-complete, and provides an effective heuristic algorithm. Our framework is also proved to guarantee an unbiased estimation for distinct contents, and achieves local differential privacy for users. Meanwhile, we also propose a randomized algorithm designed for more general cases, where the preposed knowledge for the first algorithm is unavailable. Finally, we evaluate both algorithms on real world datasets, and the results reveal that our algorithms outperform existing methods.

5/3-approximation algorithm for scheduling three-crane integrated scheduling problem

In this paper, we deal with a three-crane integrated scheduling problem arising in a finery shop of an iron and steel making enterprise. It is commonly occurred in every large iron and steel company. The main objective of this process is to determine the sequence of loading operations so that the makespan of all required refined melted steel in ladles, that is, the latest finery completion time among all ladles of the melted steel, is minimized. By exploring the problem structure, an effective heuristic algorithm is designed to solve the problem. We prove that the worst case performance of the heuristic algorithm is 5/3. The results show that the proposed heuristic algorithm is capable of generating good quality solutions.

Cooperative video caching scheme over software defined passive optical network

Localized video caching over passive optical network (PON), which means to cache videos on inner PON nodes (IPNs), i.e., optical line terminal (OLT), remote node (RN) and optical network units (ONUs), is now considered as an efficient scheme to provide better-quality video services for end users. However, given the limited local storage capacity of IPNs, the cooperation among IPNs is becoming critical for conducting localized video caching. In this paper, considering that caching videos on the RN will break the passive characteristic of PON, we study the problem of cooperative video caching (CVC) at the OLT-layer and the ONU-layer over GPON with a focus on maximizing the benefits introduced by localized video caching, under the finite local resources. We firstly design software defined GPON architectures to pave the way for efficiently implementing CVC, then we specifically study the CVC problem under two scenarios, one is that the cooperation can only be realized between the OLT and ONUs due to architecture constraint, the other is that the cooperation among all ONUs can also be enabled. In the first scenario, after proving the NP-hardness of the CVC problem, we derive a benefits upper bound for a special case with homogeneous required resources of videos of the problem, and then propose an efficient heuristic algorithm to solve the problem with general form. In the second scenario, given that the CVC problem is more complicated than that in the first scenario, we also design an effective heuristic algorithm to solve it. Simulation results validate the efficiency of the proposed methods.

Explicit Content Caching at Mobile Edge Networks with Cross-Layer Sensing.

The deployment density and computational power of small base stations (BSs) are expected to increase significantly in the next generation mobile communication networks. These BSs form the mobile edge network, which is a pervasive and distributed infrastructure that can empower a variety of edge/fog computing applications. This paper proposes a novel edge-computing application called explicit caching, which stores selective contents at BSs and exposes such contents to local users for interactive browsing and download. We formulate the explicit caching problem as a joint content recommendation, caching, and delivery problem, which aims to maximize the expected user quality-of-experience (QoE) with varying degrees of cross-layer sensing capability. Optimal and effective heuristic algorithms are presented to solve the problem. The theoretical performance bounds of the explicit caching system are derived in simplified scenarios. The impacts of cache storage space, BS backhaul capacity, cross-layer information, and user mobility on the system performance are simulated and discussed in realistic scenarios. Results suggest that, compared with conventional implicit caching schemes, explicit caching can better exploit the mobile edge network infrastructure for personalized content dissemination.

An effective heuristic algorithm for the partial shop scheduling problem

In a partial shop scheduling problem the operations of each job have to respect a partial order, which can be different for each job. We study the problem of finding a solution of minimal makespan in partial shops. This problem generalizes many problems which have been studied independently in the literature, such as the group shop scheduling problem, the mixed shop scheduling problem, and the open shop scheduling problem. In this paper we propose an algorithm which is able to find solutions for the partial shop scheduling problem. In computational experiments we find that the proposed single heuristic can compete with the state-of-the-art heuristics for the partial shop, group shop, mixed shop, and open shop, and in many cases, improves the state of the art. The main contribution of this paper is the demonstration that a single algorithm can solve effectively many special cases of the partial shop without taking into consideration their particular structure. We highlight the contribution of the main novel components of the algorithm, namely the initial solution generator, neighbourhood structure, and the lower bound for new solutions generated by such neighbourhood.