Improved Search Algorithm Research Articles

Search for interacting galaxy clusters from SDSS DR-17 employing optimized friends-of-friends algorithm and multimessenger tracers

ABSTRACT In the theoretical framework of hierarchical structure formation, galaxy clusters evolve through continuous accretion and mergers of substructures. Cosmological simulations have revealed the best picture of the universe as a 3D filamentary network of dark-matter distribution called the cosmic web. Galaxy clusters are found to form at the nodes of this network and are the regions of high merging activity. Such mergers being highly energetic, contain a wealth of information about the dynamical evolution of structures in the Universe. Observational validation of this scenario needs a colossal effort to identify numerous events from all-sky surveys. Therefore, such efforts are sparse in literature and tend to focus on individual systems. In this work, we present an improved search algorithm for identifying interacting galaxy clusters and have successfully produced a comprehensive list of systems from SDSS DR-17. By proposing a set of physically motivated criteria, we classified these interacting clusters into two broad classes, ‘merging’ and ‘pre-merging/postmerging’ systems. Interestingly, as predicted by simulations, we found that most cases show cluster interaction along the prominent cosmic filaments of galaxy distribution (i.e. the proxy for dark matter filaments), with the most violent ones at their nodes. Moreover, we traced the imprint of interactions through multiband signatures, such as diffuse cluster emissions in radio or X-rays. Although we could not find direct evidence of diffuse emission from connecting filaments and ridges; our catalogue of interacting clusters will ease locating such faintest emissions as data from sensitive telescopes such as eROSITA or SKA, becomes accessible.

An Integrated YOLOv5 and Hierarchical Human-Weight-First Path Planning Approach for Efficient UAV Searching Systems

Because the average number of missing people in our country is more than 20,000 per year, determining how to efficiently locate missing people is important. The traditional method of finding missing people involves deploying fixed cameras in some hotspots to capture images and using humans to identify targets from these images. However, in this approach, high costs are incurred in deploying sufficient cameras in order to avoid blind spots, and a great deal of time and human effort is wasted in identifying possible targets. Further, most AI-based search systems focus on how to improve the human body recognition model, without considering how to speed up the search in order to shorten the search time and improve search efficiency, which is the aim of this study. Hence, by exploiting the high-mobility characteristics of unmanned aerial vehicles (UAVs), this study proposes an integrated YOLOv5 and hierarchical human-weight-first (HWF) path planning framework to serve as an efficient UAV searching system, which works by dividing the whole searching process into two levels. At level one, a searching UAV is dispatched to a higher altitude to capture images, covering the whole search area. Then, the well-known artificial intelligence model YOLOv5 is used to identify all persons in the captured images and compute corresponding weighted scores for each block in the search area, according to the values of the identified human bodies, clothing types, and clothing colors. At level two, the UAV lowers its altitude to sequentially capture images for each block, in descending order according to its weighted score at level one, and it uses the YOLOv5 recognition model repeatedly until the search target is found. Two improved search algorithms, HWFR-S and HWFR-D, which incorporate the concept of the convenient visit threshold and weight difference, respectively, are further proposed to resolve the issue of the lengthy and redundant flight paths of HWF. The simulation results suggest that the HWF, HWFR-S, and HWFR-D search algorithms proposed in this study not only effectively reduce the length of a UAV’s search path and the number of search blocks but also decrease the search time required for a UAV to locate the search target, with a much higher search accuracy than the two traditional search algorithms. Moreover, this integrated YOLOv5 and HWF framework is implemented and tested in a real scenario to demonstrate its capability in enhancing the efficiency of a search and rescue operation.

A control strategy for improving the accuracy of lateral tracking of autonomous vehicles

Lateral tracking is a fundamental technology for autonomous vehicles, particularly in narrow and complex scenes such as parking lots and alleys, where precise control is critical. To enhance the efficacy and precision of the traditional Pure Pursuit algorithm (PP) in the lateral tracking of low-speed autonomous vehicles, this study proposes a continuous adaptive piecewise fitting method based on the cubic Bézier curve to smooth the reference path, followed by a search for the look-ahead point based on the geometric relationship between the vehicle and the reference path. An improved search algorithm based on vehicle speed and road curvature is used to identify the look-ahead point. In case of significant changes in the reference path curvature, the initially obtained look-ahead point is modified. The proposed Improve Pure Pursuit Method (IPP) is then used to calculate the steering angle of the front wheel. To validate the proposed method, the Carsim/Simulink co-simulation model and the autonomous vehicle platform are established. The simulation and real-vehicle tests demonstrate that compared with the pure pursuit algorithm based on Vehicle speed control (VPP) and Fuzzy Logic control pure Pursuit (FPP), IPP performs better in complex low-speed lateral tracking scenes. It reduces the issue of cutting corners when the vehicle enters a corner and enhances lateral tracking accuracy.

Experimental demonstration of deterministic quantum search for multiple marked states without adjusting the oracle.

Grover's search algorithm is a well-known quantum algorithm that has been extensively studied and improved to increase its success rate and enhance its flexibility. However, most improved search algorithms require an adjustment of the oracle, which may not be feasible in practical problem-solving scenarios. In this work, we report an experimental demonstration of a deterministic quantum search for multiple marked states without adjusting the oracle. A linear optical setup is designed to search for two marked states, one in a 16-state database with an initial equal-superposition state and the other in an 8-state database with different initial nonequal-superposition states. The evolution of the probability of finding each state in the database is also measured and displayed. Our experimental results agree well with the theoretical predictions, thereby proving the feasibility of the search protocol and the implementation scheme. This work is a pioneering experimental demonstration of deterministic quantum search for multiple marked states without adjusting the oracle.

Hybrid Mahalanobis Taguchi System with Binary Whale Optimisation Feature Selection for the Wisconsin Breast Cancer Dataset

The Mahalanobis-Taguchi System (MTS) is a statistical approach used in breast cancer research to facilitate early detection and promote efficient treatment. The technique analyses mammogram images for significant features using a multivariate statistical analysis technique. It combines the Mahalanobis distance (MD) and Taguchi's method to determine the differences between benign and malignant samples. While orthogonal array (OA) has been widely used in MTS, it has been criticised for providing suboptimal results due to insufficient coverage of feature combinations during the feature optimisation process. To address this issue, the Binary Whale Optimisation Algorithm (BWOA) is proposed as an improved search algorithm for MTS. This paper aims to develop a novel hybrid method that enhances the efficiency of the Mahalanobis Taguchi System (MTS). The performance of feature selection ability due to different MTS hybrid algorithms were also compared. BWOA simulates the hunting behaviour of humpback whales and works by exploring new regions of the solution space, gradually narrowing the search space, and fine-tuning the solution. MTS-BWOA demonstrated its enhanced capability in feature optimisation compared to traditional MTS methods and has the potential to be applied in other medical imaging domains.

An Improved Search Algorithm for WSN with Charging Nodes

The existing semi-centralized stochastic election approach, Stochastic Election of Appropriate Range Cluster Heads (SEARCH) is analyzed and features are improved which, boasts low time cost, improved stable period, and eliminates the unfavorable scenario, that no cluster head emerges during some specific rounds. However, the implementation of SEARCH in WSNs with solar charging nodes offers very marginal improvement in a stable period. This proves the inefficiency of the algorithm in handling these charging nodes. The improvement offered aims to make the algorithm, more energy aware and hence comparatively well organized in handling the charging nodes in the network. This improvement, equipped with dynamic tracking of instantaneous node energy shows a promising way to extend clustering algorithms to networks with chargeable nodes. In addition, the existing SEARCH algorithm’s cluster head election procedure lacks a futuristic view of the minimum energy requirement for successful operation throughout an entire round. Intermediate cluster head death can be detrimental, to network performance by paralyzing the corresponding clusters, resulting in total data transmission failure of all nodes in that cluster. Implementing the improved SEARCH algorithm drastically reduces the bit loss by 95%, with increasing the transmitted bits successfully by 4%. The overall lifetime of the WSN improves by around 30% and the number of cluster heads dying prematurely in the middle of round operation is reduced by 89%. This paper analyzes and refines a current clustering algorithm, thereby promisingly enhancing its capacity to boost the overall network performance.

RNA-As-Graphs Motif Atlas-Dual Graph Library of RNA Modules and Viral Frameshifting-Element Applications.

RNA motif classification is important for understanding structure/function connections and building phylogenetic relationships. Using our coarse-grained RNA-As-Graphs (RAG) representations, we identify recurrent dual graph motifs in experimentally solved RNA structures based on an improved search algorithm that finds and ranks independent RNA substructures. Our expanded list of 183 existing dual graph motifs reveals five common motifs found in transfer RNA, riboswitch, and ribosomal 5S RNA components. Moreover, we identify three motifs for available viral frameshifting RNA elements, suggesting a correlation between viral structural complexity and frameshifting efficiency. We further partition the RNA substructures into 1844 distinct submotifs, with pseudoknots and junctions retained intact. Common modules are internal loops and three-way junctions, and three submotifs are associated with riboswitches that bind nucleotides, ions, and signaling molecules. Together, our library of existing RNA motifs and submotifs adds to the growing universe of RNA modules, and provides a resource of structures and substructures for novel RNA design.

Improved Search Algorithm of Digital Speckle Pattern Based on PSO and IC-GN

Digital speckle correlation method has not only been widely used in a variety of photometric mechanical scenarios, but also integrated with multiple disciplines. In the future, it will even be inextricably linked to the Internet of Things, autonomous driving, deep learning and other fields. For a given hardware condition, it is of great significance to improve the efficiency of integer-pixel search and increase the accuracy and efficiency of the sub-pixel algorithm. In this paper, we propose an improved digital speckle correlation method, which consists of an integer-pixel search algorithm and a sub-pixel search algorithm. With respect to the integer-pixel search, aiming to address the two problems of uniqueness of maximum value and parameter setting of PSO-W algorithm, the algorithm PSO-1 is proposed, and the results of comparison experiments show that it has higher search efficiency. In terms of sub-pixels, based on IC-GN algorithm with the highest accuracy at present, the IV-ICGN algorithm is proposed, and the simulation experiment results show that the proposed algorithm has higher accuracy and higher efficiency than the comparison algorithm.

Implementation of efficient quantum search algorithms on NISQ computers

Despite the advent of Grover's algorithm for the unstructured search, its successful implementation on near-term quantum devices is still limited. We apply three strategies to reduce the errors associated with implementing quantum search algorithms. Our improved search algorithms have been implemented on the IBM quantum processors. Using them, we demonstrate three- and four-qubit search algorithm with higher average success probabilities compared to previous works. We present the successful execution of the five-qubit search on the IBM quantum processor for the first time. The results have been benchmarked using degraded ratio, which is the ratio between the experimental and the theoretical success probabilities. The fast decay of the degraded ratio supports our divide-and-conquer strategy. Our proposed strategies are also useful for implementation of quantum search algorithms in the post-NISQ era.

An efficient AI-based method to play the Mahjong game with the knowledge and game-tree searching strategy

The Mahjong game has widely been acknowledged to be a difficult problem in the field of imperfect information games. Because of its unique characteristics of asymmetric, serialized and multi-player game information, conventional methods of dealing with perfect information games are difficult to be applied directly on the Mahjong game. Therefore, AI (artificial intelligence)-based studies to handle the Mahjong game become challenging. In this study, an efficient AI-based method to play the Mahjong game is proposed based on the knowledge and game-tree searching strategy. Technically, we simplify the Mahjong game framework from multi-player to single-player. Based on the above intuition, an improved search algorithm is proposed to explore the path of winning. Meanwhile, three node extension strategies are proposed based on heuristic information to improve the search efficiency. Then, an evaluation function is designed to calculate the optimal solution by combining the winning rate, score and risk value assessment. In addition, we combine knowledge and Monte Carlo simulation to construct an opponent model to predict hidden information and translate it into available relative probabilities. Finally, dozens of experiments are designed to prove the effectiveness of each algorithm module. It is also worthy to mention that, the first version of the proposed method, which is named as KF-TREE, has won the silver medal in the Mahjong tournament of 2019 Computer Olympiad.

Enhancing Balanced Graph Edge Partition with Effective Local Search

Graph partition is a key component to achieve workload balance and reduce job completion time in parallel graph processing systems. Among the various partition strategies, edge partition has demonstrated more promising performance in power-law graphs than vertex partition and thereby has been more widely adopted as the default partition strategy by existing graph systems. The graph edge partition problem, which is to split the edge set into multiple balanced parts with the objective of minimizing the total number of copied vertices, has been widely studied from the view of optimization and algorithms. In this paper, we study local search algorithms for this problem to further improve the partition results from existing methods. More specifically, we propose two novel concepts, namely adjustable edges and blocks. Based on these, we develop a greedy heuristic as well as an improved search algorithm utilizing the property of max-flow model. To evaluate the performance of our algorithms, we first provide adequate theoretical analysis in terms of approximation quality. We significantly improve the previous known approximation ratio for this problem. Then we conduct extensive experiments on a large number of benchmark datasets and state-of-the-art edge partition strategies. The results show that our proposed local search framework can further improve the quality of graph partition by a wide margin.

Feast In: A Machine Learning Image Recognition Model of Recipe and Lifestyle Applications

With the increase of individuals having an interest in the culinary world, the demand for recipe and lifestyle applications have increased. As we adapt to the changes around us during these trying times, many have also taken an interest in home-cooking. However, it may be challenging, especially for beginners to brainstorm recipes for cooking as they may not be equipped with the proper ingredients to do so. In this paper, we propose Feast In, a platform for web and mobile devices which aims to meet a user’s needs for home-cooking. The platform focuses on three unique features which make Feast In more than just the average recipe platform. Firstly, an improved search algorithm which goes beyond searching for keywords would help users narrow down recipes which they can use in their kitchen. Next, customization features which would create a personalized experience, specifically towards recipes results. This would provide individuals who may face allergies or dietary restrictions an improved experience as they would not have to browse through recipes which do not meet their needs. Lastly, the search-by-image function which utilizes image recognition and machine learning technologies. Users will be able to upload an image of food that they have come across and Feast In will return a list of results which matches the image uploaded. By conducting this research, we were able to propose a unique lifestyle and recipe application which would aid users in searching for the perfect recipe.

An Improved Hough Transform Method for Detecting Forward Vehicle and Lane in Road

In this paper, we propose an integrated method for lane detection and vehicle detection, which tries to make a real-time analysis of vehicle video for identifying lane, detecting, and tracking forward vehicles. In lane detection, image preprocessing, line detection based on improved Hough transform, and straight-line model reconstruction are used. For vehicle detection, preprocessing, vehicle shadow merging based on the improved search algorithm, regions of interest (ROI) demarcation, lane determination, and vehicle tracking are used. The experiment results show that the time it takes to process an image is about 25ms. Additionally, the lane detection rate of vehicles driving on a structured road is approximately 98%, and the vehicle detection rate of the closest forward vehicle is approximately 81%.

Research on AGV Path Planning Method Based on Improved BAS Algorithm

In order to improve the speed of AGV and the ability to adapt to the environment, this article proposes an AGV path planning method based on an improved search algorithm for beetle, which is explained from three aspects: environmental modeling, path planning, and dynamic obstacle avoidance. The simulation results of path planning are based on the bas algorithm. The grid method environment simulation shows that the method has strong path search ability and strong obstacle avoidance function. It can plan out more quickly than other algorithms in very complicated obstacle environment. An optimized path for safety avoidance is proposed.

Analytics in real time surveillance video using two-bit transform accelerative regressive frame check

Face recognition is an established area of research in computer vision and it had played a great role in developing content based personal retrieval systems from real time surveillance video feeds. Face recognition in live videos is a complex problem as facial features fall into high dimensional space and involves large search time. Though, there is an extensive improvement in computational infrastructure over the years, the need for improved search algorithms without increase in cost is a challenge. Existingmethodologies in literature fail to perform in real time scenarios as the cost of feature matching is high. Hence, this research work proposes a Two-Bit Transform AccelerativeRegressive Frame Check algorithm (2BT-ARFCA) methodology that facilitates face recognition in video at a faster rate, suitable for surveillance and authentication applications. Finally the results are experimentally validated with variousvideo datasets and the state-of-the-art techniques proves that the proposed method performs better in terms of Specificity, Sensitivity, Mean Square Error (MSE), Peak signal to noise Ratio (PSNR), The Structural Similarity Index (SSIM) and accuracy.

New and improved search algorithms and precise analysis of their average-case complexity

In this paper, we consider the searching problem over ordered sequences. It is well known that Binary Search (BS) algorithm solves this problem with very efficient complexity, namely with the complexity θ(log2n). The developments of the BS algorithm, such as Ternary Search (TS) algorithm do not improve the efficiency. The rapid increase in the amount of data has made the search problem more important than in the past. And this made it important to reduce average number of comparisons in cases where the asymptotic improvement is not achieved. In this paper, we identify and analyze an implementation issue of BS. Depending on the location of the conditional operators, we classify two different implementations for BS which are widely used in the literature. We call these two implementations weak and correct implementations. We calculate precise number of comparisons in average case for both implementations. Moreover, we transform the TS algorithm into an improved ternary search (ITS) algorithm. We also propose a new Binary–Quaternary Search (BQS) algorithm by using a novel dividing strategy. We prove that an average number of comparisons for both presented algorithms ITS and BQS is less than for the case of correct implementation of the BS algorithm. We also provide the experimental results.

SPH Method with Space-Based Variable Smoothing Length and Its Applications to Free Surface Flow

In order to improve the computational efficiency and spatial resolution of smoothed particle hydrodynamics (SPH) method, a SPH method with space-based variable smoothing length has been developed. In addition, since linked-list search algorithm cannot handle the variable smoothing length problems, an improved linked-list search algorithm and a balanced alternating digital tree (B-ADT) search algorithm have been proposed. The performance of the two improved search algorithms has been evaluated in detail. These methods have been used to simulate two cases of water entry impact and two cases of gas–liquid two-phase flow. The results show that, by using space-based variable smoothing length algorithm, computational cost can be greatly reduced and the numerical accuracy is maintained.

An empirical evaluation of evolutionary algorithms for unit test suite generation

ContextEvolutionary algorithms have been shown to be effective at generating unit test suites optimised for code coverage. While many specific aspects of these algorithms have been evaluated in detail (e.g., test length and different kinds of techniques aimed at improving performance, like seeding), the influence of the choice of evolutionary algorithm has to date seen less attention in the literature. ObjectiveSince it is theoretically impossible to design an algorithm that is the best on all possible problems, a common approach in software engineering problems is to first try the most common algorithm, a genetic algorithm, and only afterwards try to refine it or compare it with other algorithms to see if any of them is more suited for the addressed problem. The objective of this paper is to perform this analysis, in order to shed light on the influence of the search algorithm applied for unit test generation. MethodWe empirically evaluate thirteen different evolutionary algorithms and two random approaches on a selection of non-trivial open source classes. All algorithms are implemented in the EvoSuite test generation tool, which includes recent optimisations such as the use of an archive during the search and optimisation for multiple coverage criteria. ResultsOur study shows that the use of a test archive makes evolutionary algorithms clearly better than random testing, and it confirms that the DynaMOSA many-objective search algorithm is the most effective algorithm for unit test generation. ConclusionOur results show that the choice of algorithm can have a substantial influence on the performance of whole test suite optimisation. Although we can make a recommendation on which algorithm to use in practice, no algorithm is clearly superior in all cases, suggesting future work on improved search algorithms for unit test generation.

Knowledge-based analytics for massively distributed networks with noisy data

This article develops and implements an improving search algorithm that effectively and efficiently identifies pathways of interest using knowledge-based analytics for massively distributed networks with noisy data. The method developed in this article fundamentally changes how critical information is extracted from large data-sets. Many methods have been developed in the past to identify structures in large graphs. Most of these methods are computationally inefficient for large graphs and their outcome depends on the graph metrics and statistical measures. There has been limited research on using optimisation techniques for data mining in large networks with noisy data. The algorithm developed in this article converges to the optimal solution by traversing the interior of a feasible region. Experiments show that it identifies a pathway of interest from a network of 160,000 components in 10 hours using parallel computing. Future work will include customisation and implementation of the method to other large networks in a variety of applications.

Decentralized Multi-UAV Flight Autonomy for Moving Convoys Search and Track

This brief is concerned with integrated autonomous takeoff, target search, task assignment, and tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) in urban environments. The problem is to design flight autonomy that is embedded onboard each UAV to enable autonomous flight coordination and distributed tasking. Control logic design based on a finite state automaton model, integrating four modes of operations, namely, the takeoff mode, the fly-to-area of operation mode, the search mode, and the tracking mode, is presented. Different from the state-of-the-art of recent research, this brief provides a preliminary research on the autonomous cooperative takeoff for miniature fixed-wing UAVs, by considering collision avoidance, communication failure, etc. To make UAVs autonomously and cooperatively search roads in the urban environments, an efficient improved search algorithm is proposed based on recent research on the coverage search in the literature. For the target tracking, using geometric relations (relative position, orientations, speed ratio, and minimal turning radius), a systematic algorithm is developed to generate an optimal online path. All the algorithms in this work are developed based on realistic miniature fixed-wing UAV dynamic models. The main focus of the brief is to test the developed control logic and also the algorithms. The proposed framework is evaluated by our 3-D multi-UAV test bed.