Parallel Search Research Articles

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.

A Memetic Algorithm for Curvature-Constrained Path Planning of Messenger UAV in Air-Ground Coordination

This paper addresses a UAV path planning problem for a team of cooperating heterogeneous vehicles composed of one unmanned aerial vehicle (UAV) and multiple unmanned ground vehicles (UGVs). The UGVs are used as mobile actuators and scattered in a large area. To achieve multi-UGV communication and collaboration, the UAV, modeled as a Dubins vehicle, serves as a messenger to fly over the effective communication range of all UGVs to relay information. The curvature-constrained path planning of the messenger UAV is formulated as a Dubins Traveling Salesman Problem with Dynamic Neighborhood (DTSPDN) which is a complex optimization problem involving coupled variables and contains dynamic constraints. We design an effective memetic algorithm to find the shortest route that enables the messenger UAV to visit all moving UGVs. This algorithm combines the genetic algorithm procedure, two kinds of local search operators based on gradient search and uniform sampling respectively, and a gradient-based repair operator to repair the solutions violating dynamic constraints. During the evolutionary process, a special phenomenon may occur that changing some decision variables (i.e., visiting sequence and location) may not affect the evaluation function value, but may alter the feasible region of another decision variable (i.e., visiting time) due to the encounter constraint between the UAV and UGV. To track and utilize the change of the feasible region, a transformation procedure is proposed to change one solution to another with less visiting time by analyzing the encounter pattern between UAV and UGV. The computational results on random instances with different scales demonstrate that the proposed approach can effectively generate better curvature-constrained tours to encounter all moving UGVs when compared to other four competitive algorithms in the literature. Note to Practitioners—This paper studies an emerging path planning problem for a UAV which is used to provide communication service for multiple moving UGVs. These UGVs are required to execute tasks (e.g., firefighting, search and rescue) within a large area. Due to their limited communication capabilities, they may be unable to obtain necessary information from other UGVs. The UAV serves as a messenger to fly over the effective communication range of all moving UGVs to relay information. We propose a novel memetic algorithm to efficiently search for the shortest tour that enables the messenger UAV to visit all moving UGVs. The memetic algorithm combines the parallel global search virtue of genetic algorithm with efficient local search procedure to improve the generated tour. A gradient-based repair procedure is also employed to make sure that the planned tour can guide the UAV to sequentially encounter each moving UGV. Simulations exhibit that the proposed approach can effectively generate high-quality tours for messenger UAV to rapidly visit all UGVs, which assists UGVs to achieve collaboration in large area. In future work, the proposed memetic algorithm will be extended to plan tours for multiple messenger UAVs.

Information gap-based coordination scheme for active distribution network considering charging/discharging optimization for electric vehicles and demand response

The power fluctuation of load and distributed generation is a leading cause of load and voltage fluctuations and directly impacts the safe and efficient operation of the active distribution network in recent years due to the growing penetration of electric vehicles and distributed generation in the network. To optimize the load characteristics, a demand response model based on a logistic function is introduced, and the dynamic charging/discharging electricity price of electric vehicles based on the comprehensive load is considered in order to incentivize electric vehicle users to charge/discharge orderly. Then, a coordinated optimal model based on the information gap decision theory is constructed to cope with the uncertainty of distributed generation and ensure the operational economy and robustness of the active distribution network. Additionally, a population-based multi-objective state transition algorithm with a parallel search mechanism and communication mechanism was proposed to solve the demand response model, and second-order cone programming was utilized to solve the coordinated optimal model with complex constraints. Finally, the proposed comprehensive model and algorithm were investigated based on the IEEE 33 test system. The results show that the proposed model not only reduces the peak-to-valley difference in load by 24.38% but also significantly reduces the total operating cost by 13.41%. Simultaneously, the proposed algorithm increases the solving efficiency, verifying the feasibility and effectiveness of the optimal strategy and algorithm, which promotes the economical and reliable operation of the active distribution network.

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.

ASA: A ccelerating S parse A ccumulation in Column-wise SpGEMM

Sparse linear algebra is an important kernel in many different applications. Among various sparse general matrix-matrix multiplication (SpGEMM) algorithms, Gustavson’s column-wise SpGEMM has good locality when reading input matrix and can be easily parallelized by distributing the computation of different columns of an output matrix to different processors. However, the sparse accumulation (SPA) step in column-wise SpGEMM, which merges partial sums from each of the multiplications by the row indices, is still a performance bottleneck. The state-of-the-art software implementation uses a hash table for partial sum search in the SPA, which makes SPA the largest contributor to the execution time of SpGEMM. There are three reasons that cause the SPA to become the bottleneck: (1) hash probing requires data-dependent branches that are difficult for a branch predictor to predict correctly; (2) the accumulation of partial sum is dependent on the results of the hash probing, which makes it difficult to hide the hash probing latency; and (3) hash collision requires time-consuming linear search and optimizations to reduce these collisions require an accurate estimation of the number of non-zeros in each column of the output matrix. This work proposes ASA architecture to accelerate the SPA. ASA overcomes the challenges of SPA by (1) executing the partial sum search and accumulate with a single instruction through ISA extension to eliminate data-dependent branches in hash probing, (2) using a dedicated on-chip cache to perform the search and accumulation in a pipelined fashion, (3) relying on the parallel search capability of a set-associative cache to reduce search latency, and (4) delaying the merging of overflowed entries. As a result, ASA achieves an average of 2.25× and 5.05× speedup as compared to the state-of-the-art software implementation of a Markov clustering application and its SpGEMM kernel, respectively. As compared to a state-of-the-art hashing accelerator design, ASA achieves an average of 1.95× speedup in the SpGEMM kernel.

Shipping network optimization of port equipment components based on improved genetic algorithm

The management of components ensures that the port equipment is in good working condition, ensure port production safety and continuity. In this paper, a port equipment component supply network optimization model that considers the characteristics of shipping network is proposed to optimize hub port selection, route choice, ship deployment, inventory optimization, etc. At the same time, an improved genetic algorithm is developed, including chromosome matrix, row column crossover genetic operation, and double-layer parallel search. The results show that the model and algorithm can effectively obtain a better solution and achieve the optimization goal.

A Modified LZW Algorithm Based on a Character String Parallel Search in Cluster-Based Telemetry Data Compression

The volume of telemetry data is gradually increasing, both because of the increasingly larger number of parameters involved, and the use of higher sampling frequencies. Efficient data compression schemes are therefore needed in space telemetry systems to improve transmission efficiency and reduce the burden of required spacecraft resources, in particular regarding their transmitter power. In our primary study, a D-CLU algorithm was proposed to perform lossless compression for telemetry data, and achieve better performance. However, a limitation of this algorithm is that the compression time may become longer when the clustering head (CH) and outlier (which are compressed by LZW algorithm) numbers increase. To reduce compression delay, this paper proposed a modified character string (MCS) parallel search strategy for LZW algorithm (denoted by MCS-based LZW). The proposed MCS-based LZW algorithm designs coding principle, dictionary update rule and search strategy according to the character string matching results. Example verification and simulation results show that the proposed algorithm can effectively decrease the dictionary search times, and thus reduce the compression time.

Target Recognition and Navigation Path Optimization Based on NAO Robot

The NAO robot integrates sensors, vision systems, and control systems. Its monocular vision system is adopted to locate the target object in the three-dimensional space of robots. Firstly, a positioning model based on monocular vision is established according to the principle of small hole perspective. Then, the position coordinates of the target center are obtained in the image coordinate system. In the model mentioned above, the relationship between position coordinates and image coordinates is established at a certain space height. According to this relationship, the two-dimensional coordinates in the image are converted into the three-dimensional coordinates in the robot coordinate system. After getting the target location, we establish the navigation map and find the optimal path under the unknown environment. Based on the simultaneous localization and the mapping (SLAM) theory, the sonar sensor of the NAO robot is used to detect the distance between the robot and the obstacles or between the robot and the end landmark. Moreover, the sonar sensor and the camera are used to distinguish the obstacle and the landmark. After the navigation map is built, the bi-directional parallel search strategy and the simulated annealing algorithm are introduced to improve the traditional artificial bee colony algorithm, and the improved artificial bee colony algorithm is proposed to find an optimal path in the navigation map. Finally, the experimental results show that based on the built environment map, the robot can find an optimal path from the origin to the landmark on the premise of avoiding obstacles.

A new method for indexing continuous IoT data flows in metric space

The continuous production of heterogeneous internet of things (IoT) data created a new challenge, which is their storage and retrieve efficiently. In this work, a new method of indexing, called threshold distance (TD), is proposed in the fog‐cloud architecture. In this method, the fog layer is divided into two levels: a clustering level and an indexing level. In the clustering fog level, the first data flow is grouped into clusters of homogenous objects using the density‐based spatial clustering applications with noise (DBSCAN) algorithm. In the indexing fog level, objects in each cluster are indexed in separated generalized hyperplane‐trees (GHT). After the clustering of the next data flow, objects are inserted in existing GHT or new GHT are created according the comparison of the distances between centers of the next clusters and those of the first clusters (took as representatives of their corresponding indexes) with a threshold distance value. To test the efficiency of the TD method, the experimental results were compared with those of our second proposed method called creation of a new tree (CNT) in which, data of each cluster is indexed in a new GHT. The experimental results show that the efficiency of the TD method in GHT construction depend on the size of the data flow. For the parallel kNN search, the TD method proved its efficiency whatever the size of the data flow.

A Highly Compact Nonvolatile Ternary Content Addressable Memory (TCAM) With Ultralow Power and 200-ps Search Operation

In this work, we developed a nonvolatile ternary content addressable memory (TCAM) with a cell size of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.01~\mu \text{m}^{{2}}$ </tex-math></inline-formula> utilizing the Ge-based memory diode (MD), which has the most area-efficient TCAM design reported. The MDs have a high current ratio between ON and OFF states and a large rectifying ratio, showing the potential usage in large-dimension TCAM arrays. Besides, the functionality of parallel search was demonstrated with a 2-bit MD-TCAM array by experiment, and the electrical characterization showed expected results. With the help of the sub-ns ultrafast measurement system, it is confirmed that the search energy of MD-TCAM could reach as low as 1.0 fJ/bit/mismatch, and one search operation can be performed within 200 ps. Furthermore, the circuit-level simulation results verified that the MD-TCAM developed in this study shows good performance in 128-bit parallel searching, which is promising for the ultrafast and low-power data search applications in the coming IoT era.

How Dyads Overcome Complexity and Misaligned Incentives in Imperfect Modular Decompositions

We investigate the search processes that dyads engage in when each human agent is responsible for one module of a complex task. Our laboratory experiment manipulates global vs. local incentives and low vs. high cross-modular interdependence. We find that dyads endogenously learn to coordinate their joint search efforts by experimenting with parallel and sequential search that, over time, give rise to coordinated repeated actions. Such collaborative search emerges despite complexity and misaligned incentives, and without a coordinating hierarchy.

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

A novel differential evolution algorithm for tone reservation based peak to average power ratio reduction technique in orthogonal frequency division multiplexing systems

A critical problem in the Orthogonal Frequency Division Multiplexing (OFDM) framework for 5G communication is the high Peak to Average Power Ratio (PAPR) of the transmitted signal. The Tone Reservation (TR) scheme is employed to minimize the OFDM signal’s PAPR in time domain. It is comprised of some reserved subcarriers to generate a Peak Reduction Tone (PRT) set. The selection of these PRT set and the optimal target clipping level are the depending parameters of the PAPR reduction performance. A novel Differential Evolution based Tone Reservation (DE-TR) scheme is proposed to find an optimal PRT set and then the PAPR is reduced by employing an Optimized Iterative Clipping and Filtering (OICF) technique. Compared to other related techniques like Clipping Control Tone Reservation (CC-TR), Curve Fitting based Tone Reservation (CF-TR) and Parallel Tabu Search Tone Reservation (PTS-TR), the proposed technique DE-TR(Pro.) contributes to a substantial PAPR reduction output with lower computational complexity and maintaining an adequate bit error rate (BER) and spectral spreading efficiency. In addition, the theoretical relationship between the Error Vector Magnitude (EVM) and PAPR as well as CCDF performance with EVM are analyzed.

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

A Hybrid Framework Model Based on Wavelet Neural Network with Improved Fruit Fly Optimization Algorithm for Traffic Flow Prediction

Accurate traffic flow prediction can provide sufficient information for the formation of symmetric traffic flow. To overcome the problem that the basic fruit fly optimization algorithm (FOA) is easy to fall into local optimum and the search method is single, an improved fruit fly optimization algorithm (IFOA) based on parallel search strategy and group cooperation strategy is proposed. The multi-swarm mechanism is introduced in the parallel search strategy, in which each subswarm is independent and multiple center positions are determined in the iterative process, thereby avoiding the problems of reduced diversity and premature convergence. To increase communication between fruit fly subswarms, the informative fruit flies selected from subswarms are guided by the randomly generated binary fruit fly to achieve the crossover operation in the group cooperation strategy. Then a hybrid framework model based on wavelet neural network (WNN) with IFOA (IFOA-WNN) for traffic flow prediction is designed, in which IFOA is applied to explore appropriate structure parameters for WNN to achieve better prediction performance. The simulation results verify that the IFOA can provide high-quality structural parameters for WNN, and the hybrid IFOA-WNN prediction model can achieve higher prediction accuracy and stability than the compared methods.

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.

Rapid Parallel Search Technology with Scanning Electron Microscope and Artificial Neural Network

ABSTRACT It is shown how to prevent and monitor the spread of any respiratory viral outbreaks by precisely and quickly identifying patients and asymptomatic carriers by nano technology. Rapid Parallel Search (RPS) derived from a combination of time-proved methods, hardware and software components such as Scanning Electron Microscopy (SEM), Artificial Neural Network (ANN) and a recognition system similar to ‘Face ID’ from ‘Apple Inc.’. High performance and classification precision (~50 s per test with 99.999% accuracy) for detecting the presence of all known viruses and microorganisms, that are hard or impossible to identify with molecular methods, are achieved through simultaneous automatic testing of hundreds of samples with scanning resolution of 0.5 nm. RPS has sufficient potential for real-time monitoring of all passengers of huge transcontinental airports and daily, precisely supervising of the vast majority of residents of a city with a population of one million or more without reagents.

Barriers and enablers to sustaining self-management behaviours after attending a self-management support intervention for type 2 diabetes: a protocol for a systematic review and qualitative evidence synthesis

Background: Attendance at self-management support interventions is associated with improved outcomes for people with type 2 diabetes. However, initial improvements are often not sustained beyond one year, which may be a result of difficulties in sustaining positive changes made to self-management behaviours. The aim of this systematic review is to synthesise qualitative research on the barriers and enablers to sustaining self-management behaviours following attendance at a self-management support intervention for type 2 diabetes. Methods: The review will use the “best fit” framework synthesis method to develop a new conceptual model of sustained behaviour change in type 2 diabetes. MEDLINE (Ovid), EMBASE (Elsevier), CINAHL (EBSCO), PsycINFO (Ovid), SCOPUS, ProQuest Dissertations and Theses, WorldCat and Open Grey will be searched to identify primary qualitative studies. A parallel search will be conducted in Google Scholar to identify relevant theories for the development of an a priori framework to synthesise findings across studies. Methodological limitations of included studies will be assessed using an adapted version of the Critical Appraisal Skills Programme tool for Qualitative Studies. A sensitivity analysis will be conducted to examine the impact of studies with methodological limitations on synthesis findings. Confidence in the synthesis findings will be assessed using the GRADE-CERQual tool. Screening, data extraction, methodological limitation assessment, synthesis and GRADE-CERQual assessment will be conducted by one author with a second author independently verifying a randomly selected 20% sample. Discussion: This review will develop a new model of sustained behaviour change in type 2 diabetes self-management. The findings can be used to inform the development of new interventions or revision of existing interventions to better support sustained engagement in type 2 diabetes self-management behaviours.

A MeerKAT, e-MERLIN, H.E.S.S., and Swift search for persistent and transient emission associated with three localized FRBs

ABSTRACT We report on a search for persistent radio emission from the one-off fast radio burst (FRB) 20190714A, as well as from two repeating FRBs, 20190711A and 20171019A, using the MeerKAT radio telescope. For FRB 20171019A, we also conducted simultaneous observations with the High-Energy Stereoscopic System (H.E.S.S.) in very high-energy gamma rays and searched for signals in the ultraviolet, optical, and X-ray bands. For this FRB, we obtain a UV flux upper limit of $1.39 \times 10^{-16}~{\rm erg\, cm^{-2}\, s^{-1}}$Å−1, X-ray limit of $\sim 6.6 \times 10^{-14}~{\rm erg\, cm^{-2}\, s^{-1}}$ and a limit on the very high energy gamma-ray flux $\Phi (E\gt 120\, {\rm GeV}) \lt 1.7\times 10^{-12}\, \mathrm{erg\, cm^{-2}\, s^{-1}}$. We obtain a radio upper limit of ∼15 $\mu$Jy beam−1 for persistent emission at the locations of both FRBs 20190711A and 20171019A with MeerKAT. However, we detected an almost unresolved (ratio of integrated flux to peak flux is ∼1.7 beam) radio emission, where the synthesized beam size was ∼ 8 arcsec size with a peak brightness of $\sim 53\, \mu$Jy beam−1 at MeerKAT and $\sim 86\, \mu$Jy beam−1 at e-MERLIN, possibly associated with FRB 20190714A at z = 0.2365. This represents the first detection of persistent continuum radio emission potentially associated with a (as-yet) non-repeating FRB. If the association is confirmed, one of the strongest remaining distinction between repeaters and non-repeaters would no longer be applicable. A parallel search for repeat bursts from these FRBs revealed no new detections down to a fluence of 0.08 Jy ms for a 1 ms duration burst.

A two-stage MPPT controller for PV system based on the improved artificial bee colony and simultaneous heat transfer search algorithm

The maximum power point tracking (MPPT) controller can monitor the voltage and current of photovoltaic (PV) array in real-time to acquire the maximum power output for battery charging in PV system. In order to improve the accuracy and rapidity of tracking process, a bionic two-stage MPPT control strategy composed of fast positioning stage and precise determination stage is proposed in this paper for optimizing the duty cycle d of DC–DC converter. Firstly, an improved artificial bee colony algorithm with simplified probability selection mechanism and novel employed bee phase is presented to balances exploration and exploitation for excellent rapidity in positioning rough search region around global peak. Then, the simultaneous heat transfer search (SHTS) algorithm is adopted for accurately acquiring the global maximum power point in obtained search region. On the one hand, the SHTS can reduce the subjectivity in artificial setting of the parameters to ensure the universality and accuracy of search process. On the other hand, the parallel search in SHTS can decrease the optimization time effectively. Finally, extensive simulation results illustrate that this two-stage MPPT strategy shows excellent performance in precisely identifying the GMPP of PV system with phenomenal rapidity among multiple peaks. Moreover, it outperforms all the counterparts in tracking speed and accuracy under partial shading conditions.