Search Speed Research Articles

A Novel Multitasking Ant Colony Optimization Method for Detecting Multiorder SNP Interactions.

Linear or nonlinear interactions of multiple single-nucleotide polymorphisms (SNPs) play an important role in understanding the genetic basis of complex human diseases. However, combinatorial analytics in high-dimensional space makes it extremely challenging to detect multiorder SNP interactions. Most classic approaches can only perform one task (for detecting k-order SNP interactions) in each run. Since prior knowledge of a complex disease is usually not available, it is difficult to determine the value of k for detecting k-order SNP interactions. A novel multitasking ant colony optimization algorithm (named MTACO-DMSI) is proposed to detect multiorder SNP interactions, and it is divided into two stages: searching and testing. In the searching stage, multiple multiorder SNP interaction detection tasks (from 2nd-order to kth-order) are executed in parallel, and two subpopulations that separately adopt the Bayesian network-based K2-score and Jensen-Shannon divergence (JS-score) as evaluation criteria are generated for each task to improve the global search capability and the discrimination ability for various disease models. In the testing stage, the G test statistical test is adopted to further verify the authenticity of candidate solutions to reduce the error rate. Three multiorder simulated disease models with different interaction effects and three real age-related macular degeneration (AMD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) datasets were used to investigate the performance of the proposed MTACO-DMSI. The experimental results show that the MTACO-DMSI has a faster search speed and higher discriminatory power for diverse simulation disease models than traditional single-task algorithms. The results on real AMD data and RA and T1D datasets indicate that MTACO-DMSI has the ability to detect multiorder SNP interactions at a genome-wide scale.Availability and implementation: https://github.com/shouhengtuo/MTACO-DMSI/.

Differentiable Automatic Data Augmentation by Proximal Update for Medical Image Segmentation

Dear editor, This letter presents an automatic data augmentation algorithm for medical image segmentation. To increase the scale and diversity of medical images, we propose a differentiable automatic data augmentation algorithm based on proximal update by finding an optimal augmentation policy. Specifically, on the one hand, a dedicated search space is designed for the medical image segmentation task. On the other hand, we introduce a proximal differentiable gradient descent strategy to update the data augmentation policy, which would increase the searching efficiency. Results of the experiments indicate that the proposed algorithm significantly outperforms state-of-the-art methods, and search speed is 10 times faster than state-of-the-art methods.

Path Planning of Multirotor UAV Based on the Improved Ant Colony Algorithm

Path planning is an important part of the unmanned aerial vehicle (UAV) to realize its autonomous capabilities. Aiming at the shortcomings of the traditional ant colony algorithm-based trajectory planning method, which has slow convergence speed and easy to fall into the local optimum, a path planning method based on the improved ant colony algorithm is proposed. First, a dynamic adjusting factor is added into the heuristic function to improve the directivity of path selection and search speed. Then, the state transition strategy is improved to solve the problem of slow convergence in the initial stage and easy to fall into local optimum in the later stage. Finally, the path inflection point is smoothly optimized through the cubic B-spline curve. Simulation results show that the improved ant colony algorithm can quickly converge to the optimal path and well adapt to the flight requirements of multirotor UAV.

A Hybrid Particle Swarm Optimization and Single-Objective Slap Swarm Optimization Algorithm Based MPPT Strategy

Under inhomogeneous irradiance conditions, multiple peak characteristic of P-V curve reduces the efficiency of conventional trackers like incremental conductance control. A hybrid particle swarm optimization and single-objective slap swarm optimization algorithm (PSO-SOSSOA) based which is applied to maximum power point tracking (MPPT) strategy is presented. The PSO-SOSSOA adopts the particle swarm optimization algorithm in the early period of the algorithm operation and a modified single-objective slap swarm optimization algorithm (SOSSOA) in the remaining period. The simulation results reveal that the PSO-SOSSOA can successfully deal with global MPPT and exhibits performance advantages in terms of efficiency, search speed, and power oscillation compared with the SOSSOA.

65‐5: AI Based Simulation and Design Space Exploration for Pixel Layout

Typical properties of the display backplane and CF are image quality such as brightness, uniformity, crosstalk, response time, and gamut. A global optimal design that satisfies these properties must search all design spaces. This paper proposes a simulation and design space exploration through an AI‐based solution. Numerous layouts are generated through AI based layout generator, and the properties are checked through Fast Simulator, which has been accelerated by replacing physics simulation with AI. Humans can check 10 designs per week, and AI can check over 1000 designs per hour. AI overpowers humans in search range, optimal path, predictive power and speed. In all design spaces, optimal results are being applied to the new QD display product development process.

Memory-Efficient Searching of Gas-Chromatography Mass Spectra Accelerated by Prescreening.

The number of metabolomics studies and spectral libraries for compound annotation (i.e., assigning possible compound identities to a fragmentation spectrum) has been growing steadily in recent years. Accompanying this growth is the number of mass spectra available for searching through those libraries. As the size of spectral libraries grows, accurate and fast compound annotation becomes more challenging. We herein report a prescreening algorithm that was developed to address the speed of spectral search under the constraint of low memory requirements. This prescreening has been incorporated into the Automated Data Analysis Pipeline Spectral Knowledgebase (ADAP-KDB) and can be applied to compound annotation by searching other spectral libraries as well. Performance of the prescreening algorithm was evaluated for different sets of parameters and compared to the original ADAP-KDB spectral search and the MSSearch software. The comparison has demonstrated that the new algorithm is about four-times faster than the original when searching for low-resolution mass spectra, and about as fast as the original when searching for high-resolution mass spectra. However, the new algorithm is still slower than MSSearch due to the relational database design of the former. The new search workflow can be tried out at the ADAP-KDB web portal.

Multistrategy Improved Whale Optimization Algorithm and Its Application.

To address the shortcomings of the whale optimization algorithm (WOA) in terms of insufficient global search ability and slow convergence speed, a differential evolution chaotic whale optimization algorithm (DECWOA) is proposed in this paper. Firstly, the initial population is generated by introducing the Sine chaos theory at the beginning of the algorithm to increase the population diversity. Secondly, new adaptive inertia weights are introduced into the individual whale position update formula to lay the foundation for the global search and improve the optimization performance of the algorithm. Finally, the differential variance algorithm is fused to improve the global search speed and accuracy of the whale optimization algorithm. The impact of various improvement strategies on the performance of the algorithm is analyzed using different kinds of test functions that are randomly selected. The particle swarm optimization algorithm (PSO), butterfly optimization algorithm (BOA), WOA, chaotic feedback adaptive whale optimization algorithm (CFAWOA), and DECWOA algorithm are compared for the optimal search performance. Experimental simulations are performed using MATLAB software, and the results show that the improved whale optimization algorithm has a better global optimization-seeking capability. The improved whale optimization algorithm is applied to the distribution network fault location of IEEE-33 nodes, and the effectiveness and accuracy of the distribution network fault zone location based on the multistrategy improved whale optimization algorithm is verified.

Fast Search Strategy for Robots in Dynamic Home Environment

In an unstructured home environment, environmental information is mostly disorganized. It is difficult for a service robot to obtain sufficient service information, which significantly hinders task execution. To solve this problem, a new object search strategy is proposed for improving the speed and accuracy of object search in a complex family environment. In this method, a family-environment knowledge graph is constructed using real environmental information and human knowledge, which plays a guiding role in task execution. The home environment is divided into three levels: functional rooms, static objects, and dynamic objects. The co-occurrence probabilities are obtained from open knowledge sources, including the probabilities between static and dynamic objects and between static objects and functional rooms. They are combined with ontology knowledge based on the home to form prior knowledge of a service robot. Inspired by the human search process, a distance function is introduced to calculate the distance between the robot and target objects for optimizing the search strategy. To improve the robustness of robotic services, we designed a probabilistic update model based on the service tasks and knowledge databases. Experimental results indicated that the proposed search strategy can significantly shorten the search time and increase the search accuracy compared with methods without prior knowledge and the distance function.

Gray wolf optimizer with bubble-net predation for modeling fluidized catalytic cracking unit main fractionator

Fluidized catalytic cracking unit (FCCU) main fractionator is a complex system with multivariable, nonlinear and uncertainty. Its modeling is a hard nut to crack. Ordinary modeling methods are difficult to estimate its dynamic characteristics accurately. In this work, the gray wolf optimizer with bubble-net predation (GWO_BP) is proposed for solving this complex optimization problem. GWO_BP can effectively balance the detectability and exploitability to find the optimal value faster, and improve the accuracy. The head wolf has the best fitness value in GWO. GWO_BP uses the spiral bubble predation method of whale to replace the surrounding hunting scheme of the head wolf, which enhances the global search ability and speeds up the convergence speed. And Lévy flight is applied to improve the wolf search strategy to update the positions of wolfpack for overcoming the disadvantage of easily falling into local optimum. The experiments of the basic GWO, the particle swarm optimization (PSO) and the GWO_BP are carried out with 12 typical test functions. The experimental results show that GWO_BP has the best optimization accuracy. Then, the GWO_BP is used to solve the parameter estimation problem of FCCU main fractionator model. The simulation results show that the FCCU main fractionator model established by the proposed modeling method can accurately reflect the dynamic characteristics of the real world.

A Quick Search Dynamic Vector-Evaluated Particle Swarm Optimization Algorithm Based on Fitness Distance

A quick search dynamic vector-evaluated particle swarm optimization algorithm based on fitness distance (DVEPSO/FD) is proposed according to the fact that some dynamic multi-objective optimization methods, such as the DVEPSO, cannot achieve a very accurate Pareto optimal front (POF) tracked after each objective changes, although they exhibit advantages in multi-objective optimization. Featuring a repository update mechanism using the fitness distance together with a quick search mechanism, the DVEPSO/FD is capable of obtaining the optimal values that are closer to the real POF. The fitness distance is used to streamline the repository to improve the distribution of nondominant solutions, and the flight parameters of the particles are adjusted dynamically to improve the search speed. Groups of the standard benchmark experiments are conducted and the results show that, compared with the DVEPSO method, from the figures generated by the test functions, DVEPSO/FD achieves a higher accuracy and clearness with the POF dynamically changing; from the values of performance indexes, the DVEPSO/FD effectively improves the accuracy of the tracked POF without destroying the stability. The proposed DVEPSO/FD method shows a good dynamic change adaptability and solving set ability of the dynamic multi-objective optimization problem.

Efficient hierarchical hash tree for OpenFlow packet classification with fast updates on GPUs

Packet classification is an important functionality of modern routers/switches, needed in packet forwarding, Quality of Service (QoS), firewall etc. In order to better utilize routers on the Internet, Software Defined Network (SDN) decouples control plane from data plane to fulfill centralized management. Based on OpenFlow standards, packet classification in SDN is designed for multi-field rules which are more complex than traditional 5-tuple rules. In the paper, we propose a novel packet classification algorithm, called hierarchical hash tree (H-HashTree), based on the two IP address fields and the 7 exact-match fields to partition rules into groups. An extended Bloom filter is also proposed to accelerate search process by skipping groups in the hash tree. To further improve the performance, H-HashTree is implemented on GPU. We tested on 100K rules including synthesized rules containing characteristics of ACL, FW, and IPC with different wildcard ratios in exact-match fields, and real OpenFlow rules from Open vSwitch. Compared with the existing state-of-the-art algorithms, CutTSS and TabTree [19][18], H-HashTree achieves the best performance on both search and update speeds. H-HashTree achieves 1.17-13.9 and 2.48-12.7 times faster in search speed and 2.03-6.0 and 1.87-4.53 times faster in rule updates from synthesized rulesets than CutTSS and TabTree, respectively. On the GPU platform, H-HashTree can achieve up to 114 MPPS in search speed and less than 0.04 usec/rule in rule updates.

A Sociotechnical Framework for Semantic Biomedical Content Authoring and Publishing

Due to the ubiquity of unstructured biomedical data, significant obstacles still remain in achieving accurate and fast access to online biomedical content. In lieu of the growing volume of biomedical content on the web, embedding semantic annotations has become key to enhancing search engine context-aware indexing, thereby improving search speeds and retrieval accuracy. We introduce Semantically: a socio-technical framework for semantic biomedical content authoring and publishing. Identifying the appropriate semantic vocabulary for biomedical content annotation is a time-consuming and technically challenging process. Semantically automates this search by recommending highly accurate annotations from a wide range of biomedical ontologies. Furthermore, the framework is integrated with a knowledge-sharing system which allows biomedical authors to collaborate on identifying precise annotations during the content authoring process. Similarly, preserving content-level semantics during and after publishing to foster semantic search remains a research challenge. Semantically addresses this barrier by extending Schema.org, a community-agreed and research engine endorsed guideline for publishing structured content on the web. gosemantically.com

A modified fireworks algorithm with dynamic search interval based on closed-loop control

Fireworks algorithm performs better in solving some complex real-world engineering optimization problems, but it like other swarm intelligent algorithms, which also have the problems of slow search speed, has low efficiency and easy to fall into local optimum. Therefore, a modified fireworks algorithm is proposed for amending these weaknesses in this paper. Firstly, a closed-loop dynamic search interval adjustment strategy based on feedback control is proposed to improve search effective. Reference selection, controller design and center determination method are designed to obtain a reasonable dynamic search interval. Then we analyze the effective key parameters and design an explosion radius adjustment method related to iteration stage. Inspired by the scale law of foraging behavior of marine predators, Lévy flight and Brownian movement are applied to generate mutation sparks to enhance the algorithm local search capability. To make high-quality fireworks produce denser sparks, an explosion intensity operator based on the ranking of each firework is presented. Finally, a large number of experiments are used to verify the performance of the proposed algorithm, compared to other well-known algorithms, results confirm the superiority of this algorithm in terms of convergence rate and global search capability.

Reduced Vision-Related Quality of Life in Dementia: A Preliminary Report

Background: Despite experimental evidence for concurrent dementia and visual impairment, there are no currently validated vision-related quality of life measures for use in this population. Objective: To establish the extent to which individuals with mild to moderate dementia self-report visual impairment and determine the efficacy of established vision-related quality of life measures for use in a dementia population. Methods: We compared vision-related quality of life in participants with mild-moderate dementia to healthy (dementia-free) older adults using two existing questionnaire measures already validated for use in older adults. These were the Visual Activities Questionnaire (VAQ) and the 25-item National Eye Institute Visual Function Questionnaire (VFQ-25). Results: Responses on both the VAQ and VFQ-25 revealed a significant effect of dementia on self-reported vision-related quality of life. Visual impairment in dementia was identified in the domains of color discrimination, disability glare, light/dark adaption, acuity/spatial vision, depth perception, peripheral vision, visual search, and visual processing speed. Factor analysis of the data suggested that existing vision-related quality of life measures, designed for use in older adult populations, are likely to provide a robust means of assessing vision-related quality of life in older adults with dementia. This is particularly true of the VAQ, for which one latent factor emerged for both dementia and dementia-free samples. Conclusion: Using existing measures designed for use in older adult populations, we have shown that people with dementia experience reduced vision-related quality of life.

A plague epidemic disease optimizing approach

In order to solve some optimization problems with multi-local optimal solutions, a plague infectious disease optimization (PIDO) algorithm is proposed by the dynamic model of plague infectious disease with pulse vaccination and time delay. In this algorithm, it is assumed that there are several villagers living in a village, each villager is characterized by some characteristics. The plague virus is prevalent in the village, and the villagers contract the infectious disease through effective contact with sick rats. The plague virus attacks is the few characteristics of the human body. Under the action of the plague virus, the growth status of each villager will be randomly transformed among 4 states of susceptibility, exposure, morbidity and recovery, thus a random search is achieved for the global optimal solution. The physical strength degree of villagers is described by the human health index (HHI). The higher the villager’s HHI index, the stronger the physique and the higher the surviving likelihood. 9 operators (S_S, S_E, E_E, E_I, E_R, I_I, I_R, R_R, R_S) are designed in the PIDO algorithm, and each operator only deals with the 1/1000∼1/100 of the total number of variables each time. The case study results show that PIDO algorithm has the characteristics of fast search speed and global convergence, and it is suitable for solving global optimization problems with higher dimensions.

A Personalized Recommendation Method for Short Drama Videos Based on External Index Features

Dramatic short videos have quickly gained a huge number of user views in the current short video boom. The information presentation dimension of short videos is higher, and it is easier to be accepted and spread by people. At present, there are a large number of drama short video messages on the Internet. These short video messages have brought serious information overload to users and also brought great challenges to short video operators and video editors. Therefore, how to process short videos quickly has become a research hotspot. The traditional episode recommendation process often adopts collaborative filtering recommendation or content-based recommendation to users, but these methods have certain limitations. Short videos have fast dissemination speed, strong timeliness, and fast hot search speed. These have become the characteristics of short video dissemination. Traditional recommendation methods cannot recommend short videos with high attention and high popularity. To this end, this paper adds external index features to extract short video features and proposes a short video recommendation method based on index features. Using external features to classify and recommend TV series videos, this method can quickly and accurately make recommendations to target customers. Through the experimental analysis, it can be seen that the method in this paper has a good effect.

Coot Optimization Algorithm for Optimal Placement of Photovoltaic Generators in Distribution Systems Considering Variation of Load and Solar Radiation

In this paper, photovoltaic generators (PVGs) are placed in radial distribution networks (RDNs) for reducing active power loss of one operation day by using three recently published metaheuristic algorithms including coot optimization algorithm (COOA), transient search algorithm (TSA), and crystal structure algorithm (CRSA). In one operation day, the variation of loads is considered, and the change of solar radiation over daytime is also taken. The study has two main contributions regarding the effectiveness of COOA: energy loss reduction and voltage improvement. COOA can reach high energy loss reduction, better solutions, and faster search speed than TSA and CRSA. In fact, COOA finds better energy loss than the algorithms by 1% and 1.77% for the IEEE 69-node system and 0.75% and 1.4% for the IEEE 85-node system. Furthermore, COOA is at least three times faster than CRSA and two times faster than TSA. As compared to a base system without PVGs, COOA can find better energy loss up to 60.96% and improve voltage up to 4.5%. Thus, COOA is a highly effective optimization tool with the optimal solution, high stability, and fast computation process for placing PVGs in RDNs.

The Kinematic Calibration of an Industrial Robot With an Improved Beetle Swarm Optimization Algorithm

The article proposes a kinematic calibration method based on an improved beetle swarm optimization algorithm for an industrial robot to finish drilling and riveting. Specifically, a preference random substitution method to improve the update strategy was presented, which comprises a new boundary-processing strategy that avoids stagnation states and local optimization traps by adopting a dynamic parameter adjustment strategy to increase the speed and stability of searches. The effectiveness of the proposed method was verified by simulations and calibration experiments involving a robot drilling and riveting system with an industrial robot KUKA KR500L340-2. In addition, comparisons were conducted with linear least-squares, particle swarm optimization, and beetle swarm optimization algorithms. According to the results of calibration simulations and experiments, the fitness function value of the proposed algorithm can decrease rapidly in the first 20 iterations, while the mean value of the end-effector position error is reduced from 2.95mm to 0.20mm by using the proposed algorithm. Compared with other three algorithms, the positioning accuracy is improved by more than 60%.

SecureEngine: Spammer classification in cyber defence for leveraging green computing in Sustainable city

In today’s era, Sustainable city is evaluated using the services provided to the society. The designing of the integral part of the society should be focused towards the benefits of people. Internet is extensively utilized by the society using Search Engines. The accuracy and time it takes for different search engines to retrieve information from a cloud computing repository around the world, varies. However, it has been discovered in the literature that webpage ranking reduces the amount of time a user spends surfing, which saves a significant amount of energy during computation and transmission across the network. The hyperlink structure of the web graph is used in most of the earlier solutions documented in the literature, which consumes a lot of energy during calculation. It may exacerbate the link leakage problem by increasing the frequency of spam pages. In light of the energy consumption of various smart gadgets, hyperlink structure alone is no longer sufficient for predicting webpage relevance. Its true importance is revealed by user surfing activity. To improve search engine accuracy and speed, it is critical to demote spam pages, lowering energy consumption. Among all the existing ranking algorithms in the literature, one of the important components is the PageRank algorithm used by Google’s ranking module. Keeping focus on these points, in this paper, various page ranking algorithms based upon supervised learning are surveyed and summarized with respect to different selected parameters and experiments performed. Using this information, a detailed taxonomy of search engine results is presented in the text. Moreover, PageRank algorithms are explored by using different supervised learning techniques applied in the existing proposals for getting and processing the results. In the nutshell, the PageRank methodology is surveyed with respect to web spam detection which is the demand of cognitive systems in smart cities.

Fast Search and Efficient Placement Algorithm for Reconfigurable Tasks on Modern Heterogeneous FPGAs

To date, only a tiny fraction of reconfigurable task placement algorithms is targeted at modern heterogeneous field-programmable gate array (FPGA) architecture, and they often focus on determining the final placement location and pursuing placement quality. Hence, their real-time performance is poor because feasible location searching and placement speed are rarely taken into consideration. In this article, we propose a fast search strategy based on characteristic target gene sequence (CTGS) and an efficient placement algorithm called prioritization-based minimum cost and marginal compact (P2MC). CTGS ascertains tasks’ feasible locations quickly by regarding the relatively few heterogeneous resources on FPGAs as search targets. P2MC first introduces prioritization heuristics based on task characteristics (PHTC) to presort tasks in order to improve the placement success rate and then select the final location according to the principle of minimum cost and marginal compact (2MC) so as to reduce the fragmentation of free space. The proposed algorithms are verified and evaluated on Xilinx’s mainstream FPGA families Virtex-5/6/7. Results show that CTGS can accelerate the search speed of tasks’ feasible locations by about four to five times, and P2MC can further balance placement speed and success rate. Compared with state-of-the-art heterogeneous task placement algorithms, P2MC can either increase both placement speed and success rate (by about 29% and 4.5%, respectively) or significantly increase the placement speed (by 20 times) at the expense of a bit of placement success rate (by only 5.8%).