Parallel Local Search Research Articles

Augmented multi-agent algorithm utilizing intelligent search range detection heuristic to solve assembly line sequencing problem: A case study in the truck industry

Car sequencing is a widely adopted approach to implicitly enhance the short-term balancing of mixed-model assembly lines in practical applications. In this research, we investigate this problem in the context of a real-world case taken from a truck final assembly line. Our study considers cross-ratio constraints that address limitations related to dependent features of the products. Additionally, to manage the distribution of violation errors in cases where predefined congestion level thresholds (soft rules) exist, we propose a piecewise linear penalty function. To tackle these considerations, we formulate a mathematical programming model aimed at minimizing the total weighted penalty incurred from violating both independent and dependent sequencing rules. As the problem falls into the NP-hard category, we develop a multi-start parallel local search algorithm based on multi-agent approach to efficiently solve it. The algorithm is augmented by incorporating a self-adaptive diversification mechanism and a novel intelligent search range detection heuristic. The proposed heuristic enables the searching agents to intelligently explore the solution space rather than relying on a purely random search. The numerical experiments are conducted using 30 real-world instances from three truck assembly lines to validate the quality of the proposed algorithm. The comparison of the results indicates that our algorithm outperforms state-of-the-art algorithms. Furthermore, computational experiments demonstrate the effectiveness of each proposed mechanism in enhancing the algorithm's performance.

A Fast FPGA Connection Router Using Prerouting-Based Parallel Local Routing Algorithm

Routing is one of the most time-consuming steps in the FPGA design process. Even if unceasing efforts have been made to accelerate FPGA routing, the existing work seldom pays attention to the underlying FPGA connection router. In this paper, we present a fast FPGA connection router called PRoute which implements a novel pre-routing based parallel local routing algorithm. Basically, PRoute pre-computes the potential routing solutions for various connection patterns on FPGAs, which can be directly used in the later practical routing. On the whole, PRoute is composed of A-star maze expansion and parallel local search. In the first part, PRoute gradually expands the maze wavefront towards the lowest-cost node to search for the target sink. For a wire-type node, PRoute invokes a fast parallel local search instead taking advantage of the pre-routing results, and hence the time-expensive maze expansion can be reduced. Particularly, it allows PRoute to call one another between A-star maze expansion and parallel local search. This enables the runtime efficiency of PRoute while ensuring its global search ability. In addition, we put forward an engineering improvement to further speed up PRoute by avoiding the exploration of block output pins. To our best knowledge, this work is the first to apply the idea of pre-routing for FPGAs. Experimental results show that PRoute achieves speedups of 1.8×, 2.4×, 3.2×, 4.1× and 5.1× with 1, 4, 8, 16 and 32 threads relative to the baseline VPR’s connection router respectively, without degrading the quality of results.

Efficient multi-objective optimization through parallel surrogate-assisted local search with tabu mechanism and asynchronous option

Multi-objective optimization is challenging for computationally expensive objectives because most optimization methods rely on a large number of function evaluations to approach the Pareto-optimal front. To this end, this article develops a novel multi-objective optimization algorithm, the Multi-Objective Parallel Local Surrogate-assisted search (MOPLS) algorithm, which combines surrogate approximation and parallel computing. In each iteration, MOPLS incorporates a tabu mechanism to determine new points for expensive evaluations via a series of independent surrogate-assisted local searches. A master–worker architecture in MOPLS allows the algorithm to conduct either synchronous or asynchronous parallel processing. On a number of benchmark problems, MOPLS outperforms recent surrogate-assisted algorithms within a limited computational budget. Empirical results from parallel experiments indicate that MOPLS can significantly improve its efficiency through parallelism, and the asynchronous MOPLS shows advantages in handling objectives with non-constant evaluation times. Finally, the better performance of MOPLS over alternatives in calibrating a complex watershed simulation model demonstrates its competitiveness in solving real-world engineering applications.

Frequent pattern-based parallel search approach for time-dependent agile earth observation satellite scheduling

To address the time-dependent agile earth observation satellite (AEOS) scheduling problem more effectively, the frequent pattern-based parallel search (FPBPS) algorithm is proposed, which is composed of a parallel local search procedure, a competition-based algorithm and operator adaptive selection procedure and the new solutions construction method based on frequent pattern. First, the algorithm and the operator are selected from the algorithm pool and operator pool and run in parallel. As a result, some high-quality and diverse solutions are obtained in a short time. Second, we update the probability of each algorithm and operator to strengthen the self-adaptive ability of the algorithm. Third, frequent pattern mining method is used to extract knowledge with respect to AEOS scheduling to construct new solutions, and parallel local search is applied to further improve these solutions. Finally, extensive experiments prove that the FPBPS algorithm has a better performance than other comparison algorithms in the quality of solution, computation time, and robustness.

Benchmark-Driven Configuration of a Parallel Model-Based Optimization Algorithm

This article introduces a benchmarking framework that allows rigorous evaluation of parallel model-based optimizers for expensive functions. The framework establishes a relationship between estimated costs of parallel function evaluations (on real-world problems) to known sets of test functions. Such real-world problems are not always readily available (e.g., confidentiality and proprietary software). Therefore, new test problems are created by Gaussian process simulation. The proposed framework is applied in an extensive benchmark study to compare multiple state-of-the-art parallel optimizers with a novel model-based algorithm, which combines ideas of an explorative search for global model quality with parallel local searches to increase function exploitation. The benchmarking framework is used to configure good batch size setups for parallel algorithms systematically based on landscape properties. Furthermore, we introduce a proof of concept for a novel automatic batch size configuration. The predictive quality of the batch size configuration is evaluated on a large set of test functions and the functions generated by Gaussian process simulation. The introduced algorithm outperforms multiple state-of-the-art optimizers, especially on multimodal problems. Additionally, it proves to be particularly robust over various problem landscapes, and performs well with all tested batch sizes. Consequently, this makes it well suited for black-box kinds of problems.

Defining Parallel Local Search Procedures with Neighborhood Combinators

Defining Parallel Local Search Procedures with Neighborhood Combinators

Car sequencing problem with cross-ratio constraints: A multi-start parallel local search

Car sequencing is a popular approach to implicitly improve the short-term balancing of a mixed-model assembly line in real-world applications. In this paper, based on a real-world case from a truck final assembly line, we study this problem considering cross-ratio constraints which concern the limitations of some dependent features of products. A mathematical programming model is developed to minimize the total weighted penalty of disrespecting the independent and dependent limitations. Since the problem is NP-hard, a multi-start parallel local search is developed to efficiently solve the problem. A comparison of the results illustrates the effectiveness of using the proposed algorithm.

Self-Tuning Lam Annealing: Learning Hyperparameters While Problem Solving

The runtime behavior of Simulated Annealing (SA), similar to other metaheuristics, is controlled by hyperparameters. For SA, hyperparameters affect how “temperature” varies over time, and “temperature” in turn affects SA’s decisions on whether or not to transition to neighboring states. It is typically necessary to tune the hyperparameters ahead of time. However, there are adaptive annealing schedules that use search feedback to evolve the “temperature” during the search. A classic and generally effective adaptive annealing schedule is the Modified Lam. Although effective, the Modified Lam can be sensitive to the scale of the cost function, and is sometimes slow to converge to its target behavior. In this paper, we present a novel variation of the Modified Lam that we call Self-Tuning Lam, which uses early search feedback to auto-adjust its self-adaptive behavior. Using a variety of discrete and continuous optimization problems, we demonstrate the ability of the Self-Tuning Lam to nearly instantaneously converge to its target behavior independent of the scale of the cost function, as well as its run length. Our implementation is integrated into Chips-n-Salsa, an open-source Java library for parallel and self-adaptive local search.

Parallelising the k-Medoids Clustering Problem Using Space-Partitioning

The k-medoids problem is a combinatorial optimisation problem with multiples applications in Resource Allocation, Mobile Computing, Sensor Networks and Telecommunications.Real instances of this problem involve hundreds of thousands of points and thousands of medoids.Despite the proliferation of parallel architectures, this problem has been mostly tackled using sequential approaches.In this paper, we study the impact of space-partitioning techniques on the performance of parallel local search algorithms to tackle the k-medoids clustering problem, and compare these results with the ones obtained using sampling.Our experiments suggest that approaches relying on partitioning scale more while preserving the quality of the solution.

Use of support vector machines with a parallel local search algorithm for data classification and feature selection

Over the last decade, the number of studies on machine learning has significantly increased. One of the most widely researched areas of machine learning is data classification. Most big data systems require a large amount of information storage for analytic purposes; however, this involves some disadvantages, such as the costs of processing and collecting data. Thus, many researchers and practitioners are working on effectively reducing the number of features used in classification. This paper proposes a method which jointly optimizes both feature selection and classification. A survey of the relevant literature shows that the vast majority of studies focus on either feature selection or classification. In this study, the proposed parallel local search algorithm both selects features and finds a classifier with high rates of accuracy. Moreover, the proposed method is capable of finding solutions for problems that have extremely high numbers of features within a reasonable computation time.

A novel parallel local search algorithm for the maximum vertex weight clique problem in large graphs

This study proposes a new parallel local search algorithm (Par-LS) for solving the maximum vertex weight clique problem (MVWCP) in large graphs. Solving the MVWCP in a large graph with millions of edges and vertices is an intractable problem. Parallel local search methods are powerful tools to deal with such problems with their high-performance computation capability. The Par-LS algorithm is developed on a distributed memory environment by using message passing interface libraries and employs a different exploration strategy at each processor. The Par-LS introduces new operators parallel(\(\omega \),1)-swap and parallel(1,2)-swap, for searching the neighboring solutions while improving the current solution through iterations. During our experiments, 172 of 173 benchmark problem instances from the DIMACS, BHOSLIB and Network Data Repository graph libraries are solved optimally with respect to the best/optimal reported results. A new best solution for the largest problem instance of the BHOSLIB benchmark (frb100-40) is discovered. The Par-LS algorithm is reported as one of the best performing algorithms in the literature for the solution of the MVWCP in large graphs.

Novel Distributed Scheduling Algorithms for mmWave Mesh Networks

This paper addresses throughput improvement in millimeter-wave (mmWave) mesh networks via two novel distributed scheduling algorithms. The first one uses packet aggregation and block acknowledgment (ACK) that were introduced in the IEEE Std 802.11e-2005 for WiFi. Specifically, a distributed time-division multiplexing scheduling algorithm, which targets increasing the network capacity via reserving as many contiguous slots as possible for each node, is proposed thus enabling packet aggregation. This algorithm achieves its goal when the operating signal-to-noise ratio (SNR) is significantly high. If that is not the case, the second proposed algorithm can be used. It is a distributed one that starts initially with a random feasible schedule determined cooperatively between nodes. The algorithm then tries to reach better feasible schedules via parallel and successive local searches without violating feasibility constraints. Extensive simulations show that the first algorithm improves the network throughput by almost [Formula: see text] compared to the well-known memory-guided directional medium access control (MDMAC) due to reducing the transmission overhead. The second proposed algorithm is shown to increase the number of reserved slots by about [Formula: see text] over MDMAC. Both algorithms are shown to either increase or almost maintain the same degree of fairness among the nodes as quantified by Jain’s fairness index.

A Parallel Local Search Algorithm for Clustering Large Biological Networks

Clustering is an effective technique that can be used to analyze and extract useful information from large biological networks. Popular clustering solutions often require user input for several algorithm options that can seem very arbitrary without experimentation. These algorithms can provide good results in a reasonable time period but they are not above improvements. We present a local search based clustering algorithm free of such required input that can be used to improve the cluster quality of a set of given clusters taken from any existing algorithm or clusters produced via any arbitrary assignment. We implement this local search using a modern GPU based approach to allow for efficient runtime. The proposed algorithm shows promising results for improving the quality of clusters. With already high quality input clusters we can achieve cluster rating improvements upto to 33%.

Parallel local search algorithms for high school timetabling problems

High school timetabling consists in assigning meetings between classes and teachers, with the goal of minimizing the violation of specific soft requirements. This family of problems has been frequently considered in the literature, but few strategies employing parallelism have been proposed. In this exploratory study, we consider two different parallel frameworks and present a thorough computational study in order to understand algorithmic decisions that are closely related to performance. Our best algorithm outperforms state-of-the-art algorithms for variants of the problem considered, indicating both the efficiency and the flexibility of the method.

A constraint-based parallel local search for the edge-disjoint rooted distance-constrained minimum spanning tree problem

Many network design problems arising in areas as diverse as VLSI circuit design, QoS routing, traffic engineering, and computational sustainability require clients to be connected to a facility under path-length constraints and budget limits. These problems can be seen as instances of the rooted distance-constrained minimum spanning-tree problem (RDCMST), which is NP-hard. An inherent feature of these networks is that they are vulnerable to a failure. Therefore, it is often important to ensure that all clients are connected to two or more facilities via edge-disjoint paths. We call this problem the edge-disjoint RDCMST (ERDCMST). Previous work on the RDCMST has focused on dedicated algorithms and therefore it is difficult to use these algorithms to tackle the ERDCMST. We present a constraint-based parallel local search algorithm for solving the ERDCMST. Traditional ways of extending a sequential algorithm to run in parallel perform either portfolio-based search in parallel or parallel neighbourhood search. Instead, we exploit the semantics of the constraints of the problem to perform multiple moves in parallel by ensuring that they are mutually independent. The ideas presented in this paper are general and can be adapted to other problems as well. The effectiveness of our approach is demonstrated by experimenting with a set of problem instances taken from real-world passive optical network deployments in Ireland, Italy, and the UK. Our results show that performing moves in parallel can significantly reduce the elapsed time and improve the quality of the solutions of our local search approach.

A spatial parallel heuristic approach for solving very large‐scale vehicle routing problems

AbstractThe vehicle routing problem (VRP) is one of the most prominent problems in spatial optimization because of its broad applications in both the public and private sectors. This article presents a novel spatial parallel heuristic approach for solving large‐scale VRPs with capacity constraints. A spatial partitioning strategy is devised to divide a region of interest into a set of small spatial cells to allow the use of a parallel local search with a spatial neighbor reduction strategy. An additional local search and perturbation mechanism around the border area of spatial cells is used to improve route segments across spatial cells to overcome the border effect. The results of one man‐made VRP benchmark and three real‐world super‐large‐scale VRP instances with tens of thousands of nodes verify that the presented spatial parallel heuristic approach achieves a comparable solution with much less computing time.

A parallel local search framework for the Fixed-Charge Multicommodity Network Flow problem

We present a parallel local search approach for obtaining high quality solutions to the Fixed Charge Multicommodity Network Flow problem (FCMNF). The approach proceeds by improving a given feasible solution by solving restricted instances of the problem where flows of certain commodities are fixed to those in the solution while the other commodities are locally optimized. We derive multiple independent local search neighborhoods from an arc-based mixed integer programming (MIP) formulation of the problem which are explored in parallel. Our scalable parallel implementation takes advantage of the hybrid memory architecture in modern platforms and the effectiveness of MIP solvers in solving small problems instances. Computational experiments on FCMNF instances from the literature demonstrate the competitiveness of our approach against state of the art MIP solvers and other heuristic methods.

Efficient parallel evolutionary algorithms for deadline-constrained scheduling in project management

Deadline-constrained scheduling in project management is a NP-hard optimisation problem with major relevance in software engineering and other real-life situations dealing with the planning of activities that must be completed before specific dates. This article introduces efficient parallel versions for two evolutionary algorithms genetic algorithm and hybrid evolutionary algorithm, to solve the deadline-constrained scheduling problem in project management. The proposed algorithms have been engineered to compute accurate solutions in reduced execution times. Specific evolutionary operators, including a parallel local search operator in the hybrid evolutionary algorithm, are proposed for efficiently solving realistic problem instances, and both a master-slave parallel strategy and a distributed subpopulation model are applied to further improve the computational efficiency and the results quality. The experimental analysis performed on both a set of standard problem instances and new large problem instances demonstrate that accurate solutions are computed by the proposed techniques, especially for the distributed subpopulation version of the hybrid evolutionary algorithm. The comparative experimental evaluation demonstrates that the parallel evolutionary algorithms are able to outperform in reduced execution times the results computed using one of the best well-known deterministic techniques for the problem, in particular when solving instances with tight deadline constraints.

Parallel Local Search to schedule communicating tasks on identical processors

Abstract This paper reports on the analysis of parallelization strategies for Local Search (LS) when the neighborhood size varies throughout the search. The Multiprocessor Scheduling Problem with Communication Delays (MSPCD) is used as benchmark for illustrating the methodology and results. The dynamic load distribution strategy implemented within a supervisor–worker framework is shown to offer the best performance. Experimental results on several sets of instances with up to 500 tasks show excellent speedups (super-linear in most cases) while preserving the quality of the final solution. The proposed parallel LS is incorporated into Multistart Local Search and Variable Neighborhood Search meta-heuristic frameworks to analyze its efficiency in a more complex environment. The comparison between the sequential and parallel versions of each meta-heuristic, using various numbers of processors, shows improvement in the solution quality within proportionally smaller CPU time.

A parallel local search in CPU/GPU for scheduling independent tasks on large heterogeneous computing systems

This article presents the parallel implementation on CPU/GPU of two variants of a stochastic local search method to efficiently solve the scheduling problem in heterogeneous computing systems. Both methods are based on a set of simple operators to keep the computational complexity as low as possible, thus allowing large instances of the scheduling problem to be efficiently addressed. The experimental analysis demonstrates that both versions of the parallel CPU/GPU stochastic local search are able to compute accurate suboptimal schedules in significantly shorter execution times than state-of-the-art schedulers, while also outperforming a recently published GPU parallel evolutionary scheduler in terms of both efficiency and solution quality.