Parallel Search Algorithm Research Articles

A parallel deep adaptive large neighbourhood search algorithm for distributed heterogeneous hybrid flow shops with mixed-model assembly scheduling

ABSTRACT Nowadays, manufacturing enterprises must have fast response and flexible production capabilities to meet personalized and diversified market demands. Mixed-model production and distributed production have become the preferred production methods for enterprises. This article studies a distributed heterogeneous hybrid flow shop scheduling problem with a mixed-model assembly line (DHHFSP-MMAL), which consists of manufacturing and assembly stages. The DHHFSP-MMAL is modelled by a mixed integer linear programming (MILP) model. Three constructive heuristics and a parallel deep adaptive large neighbourhood search (PDALNS) problem are presented. A constructive heuristic with a group strategy is employed to obtain an initial solution. Several deep destroy-and-repair operators are proposed where problem-specific greedy local search methods are applied. The PDALNS assigns weights to destroy-and-repair operators to guide the selection of operators. The parallel computing technique is introduced to increase the efficiency of training. The experiments demonstrate that the PDALNS algorithm is an efficient and effective algorithm for solving the DHHFSP-MMAL problem.

Parallel Beam Search Algorithms for Domain-Independent Dynamic Programming

Domain-independent dynamic programming (DIDP), a model-based paradigm based on dynamic programming, has shown promising performance on multiple combinatorial optimization problems compared with mixed integer programming (MIP) and constraint programming (CP). The current DIDP solvers are based on heuristic search, and the state-of-the-art solver, complete anytime beam search (CABS), uses beam search. However, the current DIDP solvers cannot utilize multiple threads, unlike state-of-the-art MIP and CP solvers. In this paper, we propose three parallel beam search algorithms and develop multi-thread implementations of CABS. With 32 threads, our multi-thread DIDP solvers achieve 9 to 39 times speedup on average and significant performance improvement over the sequential solver, finding the new best solutions for two instances of the traveling salesperson problem with time windows. In addition, our solvers outperform multi-thread MIP and CP solvers in four of the six combinatorial optimization problems evaluated.

Parallel path detection for fraudulent accounts in banks based on graph analysis.

This article presents a novel parallel path detection algorithm for identifying suspicious fraudulent accounts in large-scale banking transaction graphs. The proposed algorithm is based on a three-step approach that involves constructing a directed graph, shrinking strongly connected components, and using a parallel depth-first search algorithm to mark potentially fraudulent accounts. The algorithm is designed to fully exploit CPU resources and handle large-scale graphs with exponential growth. The performance of the algorithm is evaluated on various datasets and compared with serial time baselines. The results demonstrate that our approach achieves high performance and scalability on multi-core processors, making it a promising solution for detecting suspicious accounts and preventing money laundering schemes in the banking industry. Overall, our work contributes to the ongoing efforts to combat financial fraud and promote financial stability in the banking sector.

Active equalization for lithium-ion battery pack via data-driven residual charging capacity estimation

Considering the limitations in existing voltage-based and state-of-charge (SOC)-based active equalization strategies, including the difficulty in threshold value determination for equalization system on/off controlling, repeated estimation for equalization variable and the corresponding tremendous complexity, this paper designs a novel residual capacity-based active equalization strategy via data-driven residual charging capacity (RCC) estimation to fully charge all in-pack cells during constant current (CC) charging stage. Firstly, taking charge accumulation corresponding to specific voltage window as input, we build a data-driven RCC estimation model to online monitor each cell's RCC at specific voltage. Afterwards, considering the initial cell inconsistences, each cell's current RCC before equalization is further computed based on the estimated RCC and the subsequently measured charge accumulation. Finally, a parallel global search algorithm named particle swarm optimization is adopted to search the optimal combination of in-pack cells' equalization current considering equalization time and energy loss simultaneously. The verification results based on the Oxford battery degradation dataset demonstrate that the established data-driven model can realize accurate RCC estimation and all in-pack cells' totally charged capacity can roughly approach their maximum capacity at the end of CC charging stage using the proposed equalization strategy. Meanwhile, the maximum cell voltage difference and maximum cell SOC difference can be limited below 0.01V and 0.02, respectively. Moreover, by extending the charging time by only about 4min, the developed equalization strategy can further increase battery pack capacity by about 10%.

Mediating governance goals with patients and nurses satisfaction: a multi-actor multi-objective problem including fairness

ABSTRACT Balancing conflicting goals among different stakeholders is a challenging problem in various application domains. In this paper, we analyse it in the context of home healthcare. Fairness objective functions for nurses and patients are combined with system-level governance goals of the territorial centers in charge of the assistance service. From the solution of several multi-objective problems including one objective for each actor hierarchically ordered, the best goal for each stakeholder is identified and interesting managerial conclusions are drawn. To efficiently solve large-size instances, we introduce a parallel Adaptive Large Neighborhood Search algorithm with destroy and repair operators customised to solve multi-objective multi-actor problems. The algorithm proves to be highly efficient and effective when compared to a commercial Mixed Integer Programming solver, either in its plain form or enforced by a mild start and a primal heuristic. Additionally, we devise a metaheuristic method to generate the Pareto frontier of an instance.

A new parallel tabu search algorithm for the optimization of the maximum vertex weight clique problem

SummaryThe efficiency of metaheuristic algorithms depends significantly on the number of fitness value evaluations performed on candidate solutions. In addition to various intelligent techniques used to obtain better results, parallelization of calculations can substantially improve the solutions in cases where the problem is NP‐hard and requires many evaluations. This study proposes a new parallel tabu search method for solving the Maximum Vertex Weight Clique Problem (MVWCP) on the Non‐Uniform Memory Access (NUMA) architectures using the OpenMP parallel programming paradigm. Achieving scalability in the NUMA architectures presents significant challenges due to the high complexity of their memory systems, which can lead to performance loss. However, our proposed Tabu‐NUMA algorithm provides up to speed‐up with 64 cores for ten basic problem instances in DIMACS‐W and BHOSLIB‐W benchmarks. And it improves the performance of the serial Multi Neighborhood Tabu Search (MN/TS) algorithm for 38 problem instances in DIMACS‐W and BHOSLIB‐W benchmarks. We further evaluate our algorithm on larger datasets with thousands of edges and vertices from Network Data Repository benchmark problem instances, and we report significant improvements in terms of speed up. Our results confirm that the Tabu‐NUMA algorithm is among the best recent algorithms for solving MVWCP on the NUMA architectures.

An efficient VLSI architecture for fast motion estimation by hybridizing parallel spiral and adaptive threshold star diamond search algorithm‐based search algorithm in HEVC

SummaryThis article proposes a high efficiency video coding (HEVC) standard hardware using block matching motion estimation algorithm. A hybrid parallel spiral and adaptive threshold star diamond search algorithm (Hyb PS‐ATSDSA) proposes for fast motion estimation in HEVC. Parallel spiral search approach utilizes spiral pattern for searching from center to the surroundings and adaptive threshold SDA consists of two phases, they are adaptive threshold and star diamond algorithm. To lower computational complexities in HEVC architecture, parallel spiral search algorithm uses several blocks matching schemes. Adaptive threshold and star diamond algorithm are used to reduce the matching errors and remove the invalid blocks early from the procedure of motion estimation and finally predicts the final motion of the image. Speed is increased while using this hybrid algorithm. Proposed structure is carried out in Xilinx; ISE 14.5 design suit, then the experimental outcomes are analyzed to existing motion estimation strategies in field‐programmable gate array devices. Experimental performance of the proposed Hyb‐PS‐ATSDSA‐ME‐HEVC method attains lower delay 33.97%, 32.97%, 62.97, and 26.97%, and lower area 34.867%, 45.97%, 27.97%, and 43.967% compared with the existing methods, such as FSA‐ME‐HEVC, TZSA‐ME‐HEVC, hyb TZSA‐IME‐HEVC, and IBMSA‐ME‐HEVC, respectively.

Discovery of User Groups Densely Connecting Virtual and Physical Worlds in Event-Based Social Networks

An essential task of the event-based social network (EBSN) platform is to recommend events to user groups. Usually, users are more willing to participate in events and interest groups with their friends, forming a particularly closely connected user group. However, such groups do not explicitly exist in EBSN. Therefore, studying how to discover groups composed of users who frequently participate in events and interest groups in EBSN has essential theoretical and practical significance. This article proposes the problem of discovering maximum k fully connected user groups. To address this issue, this article designs and implements three algorithms: a search algorithm based on Max-miner (MMBS), a search algorithm based on two vectors (TVBS) and enumeration tree, and a divide-and-conquer parallel search algorithm (DCPS). The authors conducted experiments on real datasets. The comparison of experimental results of these three algorithms on datasets from different cities shows that the DCPS algorithm and TVBS algorithm significantly accelerate their computational time when the minimum support rate is low. The time consumption of DCPS algorithm can reach one tenth or even lower than that of MMBS algorithm.

Bayesian ensemble machine learning-assisted deterministic and stochastic groundwater DNAPL source inversion with a homotopy-based progressive search mechanism

A hyperheuristic homotopy algorithm (HH-HA) and a homotopy-based swarm intelligence algorithm for parallel stochastic search are proposed to improve the search ergodicity and stability of deterministic and stochastic inversion approaches, respectively, for the identification of dense nonaqueous phase liquid (DNAPL) source characteristics and contaminant transport parameters in groundwater. Meanwhile, a Bayesian ensemble machine learning (BEML) method for numerical simulation surrogate modeling is proposed to enhance the learning and generalization capability, while significantly reducing the computational cost of repetitive CPU-demanding likelihood evaluations in inversion iterations. Two cases are presented for the verification and application of the proposed inverse modeling methods. The results show that the BEML surrogate model is powerful in approximating the numerical model outputs, reducing the mean relative errors of the contaminant concentration and pressure head predictions to 1.18% and 1.29%, respectively. The homotopy-based progressive search mechanism is highly effective in achieving more accurate identification values for deterministic inversion and a more reliable posterior distribution for stochastic inversion. The HH-HA shows stable performance, approaching the global optimum in wide areas and reducing the mean relative error of identification from 11.95% to 4.11%. The homotopy-based parallel stochastic search lines adequately cover the search space tracking the change of homotopy parameter, and the mean relative error of point estimation is reduced to 2.03%. Though the deterministic inversion approach can provide intuitive identification values, the reliability of the inversion outputs cannot be quantified. In contrast, the proposed stochastic inversion system is of great significance for providing decision makers with comprehensive reference information.

GePA*SE: Generalized Edge-Based Parallel A* for Slow Evaluations

Parallel search algorithms have been shown to improve planning speed by harnessing the multithreading capability of modern processors. One such algorithm PA*SE achieves this by parallelizing state expansions, whereas another algorithm ePA*SE achieves this by effectively parallelizing edge evaluations. ePA*SE targets domains in which the action space comprises actions with expensive but similar evaluation times. However, in a number of robotics domains, the action space is heterogenous in the computational effort required to evaluate the cost of an action and its outcome. Motivated by this, we introduce GePA*SE: Generalized Edge-based Parallel A* for Slow Evaluations, which generalizes the key ideas of PA*SE and ePA*SE, i.e., parallelization of state expansions and edge evaluations, respectively. This extends its applicability to domains that have actions requiring varying computational effort to evaluate them. The open-source code for GePA*SE, along with the baselines, is available here: https://github.com/shohinm/parallel_search

A-ePA*SE: Anytime Edge-Based Parallel A* for Slow Evaluations

Anytime search algorithms are useful for planning problems where a solution is desired under a limited time budget. Anytime algorithms first aim to provide a feasible solution quickly and then attempt to improve it until the time budget expires. On the other hand, parallel search algorithms utilize the multithreading capability of modern processors to speed up the search. One such algorithm, ePA*SE (Edge-Based Parallel A* for Slow Evaluations), parallelizes edge evaluations to achieve faster planning and is especially useful in domains with expensive-to-compute edges. In this work, we propose an extension that brings the anytime property to ePA*SE, resulting in A-ePA*SE. We evaluate A-ePA*SE experimentally and show that it is significantly more efficient than other anytime search methods. The open-source code for A-ePA*SE, along with the baselines, is available here: https://github.com/shohinm/parallel_search

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.

Determining approximate solution of partial differential equations using radial basis function model trained with parallel symbiotic organisms search algorithm

SummaryPartial differential equations (PDEs) find extensive applications in geophysics (weather and climate modeling), astrophysics, and quantum mechanics. Solving PDEs by the conventional methods at times becomes difficult due to the complexity involved in these real‐world problems. Thus instead of an exact solution determining an approximate solution is also helpful for solving the problems. Here a model based on radial basis function (RBF) trained with parallel symbiotic organism search (PSOS) optimization is proposed to find out an approximate solution. The PSOS is a nature‐inspired optimization algorithm that ensures better accuracy due to the potential exploration of agents in the search space. Simultaneously, the computational complexity of this algorithm is low due to inbuilt parallelism. Simulation studies are reported for five real applications of PDEs: 2D modified Helmholtz equation, Poisson problem with Dirichlet boundary condition, elliptic PDE equation, and two convection‐diffusion equations. Comparison is carried out with the same RBF model parameters trained with parallel social spider optimization, original symbiotic organisms search algorithm, real coded genetic algorithm, and particle swarm optimization. The proposed model achieves minimum root mean square errors in most of the cases and achieves it with lower run time compared to the existing algorithms.

Segmentation on remote sensing imagery for atmospheric air pollution using divergent differential evolution algorithm.

Air pollution is a global issue causing major health hazards. By proper monitoring of air quality, actions can be taken to control air pollution. Satellite remote sensing is an effective way to monitor global atmosphere. Various sensors and instruments fitted to satellites and airplanes are used to obtain the radar images. These images are quite complex with various wavelength differentiated by very close color differences. Clustering of such images based on its wavelengths can provide the much-needed relief in better understanding of these complex images. Such task related to image segmentation is a universal optimization issue that can be resolved with evolutionary techniques. Differential Evolution (DE) is a fairly fast and operative parallel search algorithm. Though classical DE algorithm is popular, there is a need for varying the mutation strategy for enhancing the performance for varied applications. Several alternatives of classical DE are considered by altering the trial vector and control parameter. In this work, a new alteration of DE technique labeled as DiDE (Divergent Differential Evolution Algorithm) is anticipated. The outcomes of this algorithm were tested and verified with the traditional DE techniques using fifteen benchmark functions. The new variant DiDE exhibited much superior outcomes compared to traditional approaches. The novel approach was then applied on remote sensing imagery collected form TEMIS, a web based service for atmospheric satellite images and the image was segmented. Fuzzy Tsallis entropy method of multi-level thresholding technique is applied over DiDE to develop image segmentation. The outcomes obtained were related with the segmented results using traditional DE and the outcome attained was found to be improved profoundly. Experimental results illustrate that by acquainting DiDE in multilevel thresholding, the computational delay was greatly condensed and the image quality was significantly improved.

On Solving the Problem of Finding Kinetic Parameters of Catalytic Isomerization of the Pentane-Hexane Fraction Using a Parallel Global Search Algorithm

This article is devoted to the problem of developing a kinetic model of a complex chemical reaction using a parallel optimization method. The design of the kinetic model consists of finding the kinetic parameters of the reaction, which cannot be calculated analytically, and since the chemical reaction involves many stages, the optimization problem is multiextremal. As a chemical reaction, the process of catalytic isomerization of the pentane-hexane fraction is considered, which is now important due to the switch of the oil refining industry to the production of gasoline corresponding to the Euro-5 standard. On the basis of known industrial data on the concentrations of reaction components and the temperature at the outlet of the third reactor, the activation energies and pre-exponential factors of each reaction stage were calculated. To solve the optimization problem, the authors developed a parallel global search algorithm and a program based on Lipschitz optimization. The kinetic parameters found made it possible to develop a mathematical model of the process, which is in good agreement with industrial data. The developed mathematical model in future works will make it possible to study the dynamics of the gas–liquid flow in the reactor unit, taking into account diffusion and heat exchange processes through the catalyst layer.

Parallel Greedy Search for Random Access in Wireless Networks

We consider the random access problem in wireless networks such as cellular networks and massive machine type communication networks, where a large number of devices/users are connected to a base station. In order to establish communication with the base station, as a part of the random access procedure, active users select and transmit codes (pilot sequences) randomly from a codebook. Since the active users are typically a small fraction of the total users, sparse signal recovery algorithms are employed to detect the transmitted codes. In this paper, we use multiple measurement vector model for the received signal and develop a parallel greedy search algorithm and it’s stopping criteria to detect the active users. We show through simulations that the proposed scheme, compared with the existing greedy algorithms, gives better detection performance at lower false alarms levels. We study how the code length and the code word size need to be chosen in order to detect a given number of active users. We also address the scenario when there are different timing offsets for signals received from different users.

EPA*SE: Edge-Based Parallel A* for Slow Evaluations

Parallel search algorithms harness the multithreading capability of modern processors to achieve faster planning. One such algorithm is PA*SE (Parallel A* for Slow Expansions), which parallelizes state expansions to achieve faster planning in domains where state expansions are slow. In this work, we propose ePA*SE (Edge-Based Parallel A* for Slow Evaluations) that improves on PA*SE by parallelizing edge evaluations instead of state expansions. This makes ePA*SE more efficient in domains where edge evaluations are expensive and need varying amounts of computational effort, which is often the case in robotics. On the theoretical front, we show that ePA*SE provides rigorous optimality guarantees. In addition, ePA*SE can be trivially extended to handle an inflation weight on the heuristic resulting in a bounded suboptimal algorithm w-ePA*SE (Weighted ePA*SE) that trades off optimality for faster planning. On the experimental front, we validate the proposed algorithm in two different planning domains: 1) motion planning for 3D humanoid navigation and 2) task and motion planning for a dual-arm robotic assembly task. We show that ePA*SE can be significantly more efficient than PA*SE and other alternatives. The open-source code for ePA*SE along with the baselines is available here: https://github.com/shohinm/parallel_search

MPLP: Massively Parallelized Lazy Planning

Lazy search algorithms have been developed to efficiently solve planning problems in domains where the computational effort is dominated by the cost of edge evaluation. The existing algorithms operate by intelligently balancing computational effort between searching the graph and evaluating edges. However, they are designed to run as a single process and do not leverage the multithreading capability of modern processors. In this work, we propose a massively parallelized, bounded suboptimal, lazy search algorithm (MPLP) that harnesses modern multi-core processors. In MPLP, searching of the graph and edge evaluations are performed completely asynchronously in parallel, leading to a drastic improvement in planning time. We validate the proposed algorithm in two different planning domains: 1) motion planning for 3D humanoid navigation and 2) task and motion planning for a robotic assembly task. We show that MPLP outperforms the state-of-the-art lazy search as well as parallel search algorithms. The open-source code for MPLP is available here: https://github.com/shohinm/parallel_search

Cuckoo search algorithm with fuzzy logic and Gauss–Cauchy for minimizing localization error of WSN

The location of the sensor node is critical in wireless sensor networks (WSN) as the information acquired by the sensor node may be worthless without knowing its source. However, high accurate positioning of sensor nodes remains a big challenge. To overcome the barrier, an improved cuckoo search algorithm with fuzzy logic and Gauss–Cauchy strategy (ICS-FG) is proposed, that integrates the meta-heuristic algorithm with the traditional method. To regulate the dynamic adjustment of parameters, our study proposes a fuzzy logic based on population diversity. The proposed Gauss–Cauchy strategy significantly improve the algorithm’s search accuracy while enhancing its robustness when evaluated on several selected benchmark functions and locating unknown nodes in WSN. Experimental results obtained from well-known benchmark functions demonstrate the advance of the ICS-FG approach over the parallel compact cuckoo search algorithm (pcCS), improved adaptive genetic algorithm (IAGA), and other remarkable methods. By quantitative assessment, the proposed ICS-FG approach achieves a lower positioning error than pcCS, IAGA, and other state-of-the-art algorithms.

Image Representational Path of Regional Cultural and Creative Products Based on Genetic Algorithm.

Wenchuang product is the abbreviation of cultural and creative products, which is a modern extension of traditional culture. Its dissemination is conducive to enhancing the popularity, appeal, and influence of Chinese traditional culture. As an important carrier and manifestation of cultural and creative industries, Wenchuang products are the integration of regional cultural industry development and the dominant presentation of regional politics, culture, and economy. As a highly parallel, random, and adaptive search algorithm, genetic algorithm has been widely used in personalized product design, conceptual design, product optimization design, and intelligent design, etc. Based on the genetic algorithm, this paper studies the image representation of regional cultural and creative products. In the field of image representation design of regional cultural and creative products, tree structure coding, real number coding, binary coding, etc. are widely used in this field, and these coding methods all meet the innovative requirements of designers. In the field of image representation design of regional cultural and creative products, genetic coding, as a very important part of applying genetic algorithm, plays a very important role in its research and application.