Search Algorithm Research Articles

Industrial Robot Trajectory Optimization Based on Improved Sparrow Search Algorithm

This paper proposes an enhanced multi-strategy sparrow search algorithm to optimize the trajectory of a six-axis industrial robot, addressing issues of low efficiency and high vibration impact on joints during operation. Initially, the improved D-H parametric method is employed to establish both forward and inverse kinematic models of the robot. Subsequently, a 3-5-3 mixed polynomial interpolation trajectory planning approach is applied to the robot. Building upon the conventional sparrow algorithm, a two-dimensional Logistic chaotic system initializes the population. Additionally, a Levy flight strategy and nonlinear adaptive weighting are introduced to refine the discoverer position update operator, while an inverse learning strategy enhances the vigilante position update operator. These modifications boost both the local and global search capabilities of the algorithm. The improved sparrow algorithm, based on 3-5-3 hybrid polynomial trajectory planning, is then used for the time-optimal trajectory planning of the robot. This is compared with traditional sparrow search algorithm and particle swarm algorithm optimization results. The findings indicate that the proposed enhanced sparrow search algorithm outperforms both the standard sparrow algorithm and the particle swarm algorithm in terms of convergence speed and accuracy for robot trajectory optimization. This can lead to the increased work efficiency and performance of the robot.

LPI-based resource allocation strategy for multiple targets tracking in CMIMO radar system with array division

Considering the scenario of multiple targets equipped with interceptors in the modern electronic warfare environment, an LPI-based Resource Allocation Strategy (LPIRAS) for multiple targets tracking (MTT) in collocated MIMO (CMIMO) radar is proposed in this paper, where subarray number is exploited in the low probability of intercept (LPI) optimization problem for the first time. As the problem is formulated as a nonconvex and nonlinear optimization problem, the array power, target-beam assignment, subarray number, and subarray-beam assignment are corporately managed through a methodology incorporating the two-stage search algorithm (TSA), permutation-based target swarm generation algorithm (PTSGA) and adaptive extra subarray assignment algorithm (AESAA), resulting the LPIRAS. Finally, numerical simulations show the proposed strategy is effective and real-time. In comparison to other benchmarks, the LPIRAS achieves superior LPI performance.

Exploring Complicated Search Spaces With Interleaving-Free Sampling.

Conventional neural architecture search (NAS) algorithms typically work on search spaces with short-distance node connections. We argue that such designs, though safe and stable, are obstacles to exploring more effective network architectures. In this brief, we explore the search algorithm upon a complicated search space with long-distance connections and show that existing weight-sharing search algorithms fail due to the existence of interleaved connections (ICs). Based on the observation, we present a simple-yet-effective algorithm, termed interleaving-free neural architecture search (IF-NAS). We further design a periodic sampling strategy to construct subnetworks during the search procedure, avoiding the ICs to emerge in any of them. In the proposed search space, IF-NAS outperforms both random sampling and previous weight-sharing search algorithms by significant margins. It can also be well-generalized to the microcell-based spaces. This study emphasizes the importance of macrostructure and we look forward to further efforts in this direction. The code is available at github.com/sunsmarterjie/IFNAS.

Photonic quantum walk with ultrafast time-bin encoding

The quantum walk (QW) has proven to be a valuable testbed for fundamental inquiries in quantum technology applications such as quantum simulation and quantum search algorithms. Many benefits have been found by exploring implementations of QWs in various physical systems, including photonic platforms. Here, we propose a platform to perform quantum walks based on ultrafast time-bin encoding (UTBE) and all-optical Kerr gating. This platform supports the scalability of quantum walks to a large number of steps and walkers while retaining a significant degree of programmability. More importantly, ultrafast time bins are encoded at the picosecond time scale, far away from mechanical fluctuations. This enables the scalability of our platform to many modes while preserving excellent interferometric phase stability over extremely long periods of time without requiring active phase stabilization. Our 18-step QW is shown to preserve interferometric phase stability over a period of 50 h, with an overall walk fidelity maintained above 95%.

Semantic Web-Based Energy-Efficient Design of Dual-Mode Vehicles Based on Deep Neural Networks

Dual-Mode Vehicles (DMVs) represent a new and sustainable solution to the problems of urban mobility, and energy consumption. This proposed work of a DMV can operate without a conventional battery. Instead, it uses a combination of energy management technology, renewable energy systems, and other energy sources to ensure continuous operation. The efficiency and the working statistics of such DMV could also be sufficiently monitored through semantic techniques. The user can freely choose the type of working of the vehicle based on the current inputs given by the users. These semantics serve a crucial role in establishing a reference model for seamless data exchange between the trained Bald Eagle Search (BES) algorithm and the deep neural network (DNN). This proposed work has been implemented and the performance has been analyzed concerning the State Of Charging (SOC), generator voltage, and DC-DC converter voltage. The proposed work yields higher SOC and voltage than other existing schemes.

Research on a Fault Feature Extraction Method for an Electric Multiple Unit Axle-Box Bearing Based on a Resonance-Based Sparse Signal Decomposition and Variational Mode Decomposition Method Based on the Sparrow Search Algorithm.

In light of the issue that the vibration signal from an axle-box bearing collected during the operation of an electric multiple unit (EMU) is seriously polluted by background noise, which leads to difficulty in identifying fault characteristic frequency, this paper proposes a resonance-based sparse signal decomposition (RSSD) and variational mode decomposition (VMD) method based on sparrow search algorithm (SSA) optimization to extract the fault characteristic frequency of the bearing. Firstly, the RSSD method is utilized to decompose the signal based on the obtained optimal combination of quality factors, resulting in the optimal low-resonance component with periodic fault information. Then, the VMD method is performed on this low-resonance component. The parameter combinations for both methods are optimized utilizing the SSA method. Subsequently, envelope demodulation is applied to the intrinsic mode function (IMF) with maximum kurtosis, and fault diagnosis is achieved by comparing it with the theoretical fault characteristic frequency. Finally, experimental validation and comparison are conducted by utilizing simulated signals and example signals. The results demonstrate that the proposed method extracts more obvious periodic fault impact components. It effectively filters out the interference of complex noise and reduces the blindness of setting weights on parameters due to human experience, indicating excellent adaptability and robustness.

Accurate and fast autofocusing in off-axis digital holography based on step reduction search and particle swarm optimization

The accurate reconstruction distance is one of the important parameters in digital holography. Typical autofocusing methods have been proposed for digital holography to determine the focusing plane. In these methods, a series of holograms at fixed-step intervals always needed to be reconstructed and then locate the focal plane by using an evaluation function, a procedure that is very time-consuming. In this paper, an accurate and fast autofocusing method for off-axis digital holograms is proposed. The method first employs the step reduction search algorithm and the integral amplitude modulus (AMP) evaluation function with low computational effort to iteratively reduce the searching range of the focal plane. Then the particle swarm optimization (PSO) algorithm and a difference-in-amplitude (DIF) evaluation function with high sensitivity are used to accurately locate the focal plane. Numerical and experimental results show that the proposed method significantly improves the focusing plane detection accuracy and computational efficiency compared with the typical autofocusing methods.

Revisiting the N=1 supersymmetric trinification models from intersecting D6-branes

We revisit the construction of the N=1 supersymmetric trinification models with gauge group U(3) C × U(3) L × U(3) R in Type IIA string theory on T6/(Z2×Z2) orientifold with intersecting D6-branes. The non-trivial K-theory conditions and tadpole cancellation conditions severely restrict the number of allowed models even for the case of rectangular two-tori. Using a supervised search algorithm, we find a few four-family models where the highest wrapping number is 2. For these models, we present the complete particle spectra and the gauge coupling relations at the string-scale.

Techno-economic optimization of photovoltaic (PV)-inverter power sizing ratio for grid-connected PV systems

- The accurate sizing of the inverter, specifically the power sizing ratio (PSR) plays a vital role in maximizing energy production and economic benefits. Existing studies often overlook the complex interplay between maximizing energy capture and minimizing inverter-related economic costs when selecting the optimal PSR. This research presents a techno-economic approach to optimizing the PSR for grid-connected photovoltaic (PV) systems. A simulation model is developed, incorporating real weather data and inverter efficiency curves. The model is calibrated using a Pattern Search Algorithm (PSA) to ensure an accurate representation of real-world inverter behavior by achieving a minimum relative difference of 13.78 % (Norm-RMS) in AC power output. The analysis explores the trade-off between PSR, annual energy yield, and inverter clipping. An optimal PSR of 1.19 is identified, balancing energy capture (up to 2000W inverter capacity) and economic efficiency. This approach promotes cost-effective inverter selection and wider PV adoption.

An improved random forest model for music genre classification algorithm based on sparrow search algorithm

In this study, a random forest model optimised based on the sparrow search algorithm is explored for the music genre classification problem. By introducing the sparrow search algorithm, the model is able to search the feature space more comprehensively during the training process, which improves the learning ability of complex data relationships. After 15 iterations of optimisation, the fitness value of the model is improved from 0.2002 to 0.197, showing better convergence. The performance on the training set shows that the model predicts 2,543 music genres with an accuracy of 99.59% and only 10 incorrect predictions, which verifies its excellent performance on the training data. While on the test set, the model's prediction accuracy for 623 music genres is 79.78%, which is slightly lower than the result of the training set, but still reflects good generalisation ability. The results of this paper imply that the improved random forest model based on the sparrow search algorithm has potential application prospects in the music genre classification task. By optimising the parameter settings and feature selection, we not only improve the prediction accuracy and generalisation ability of the model, but also provide new ideas for solving other complex data classification problems. In the future, we can further expand the sample size, try different feature combinations, and conduct in-depth research by combining more domain knowledge in order to further improve the model performance and promote the development of related fields.

Optimization of Joint Scheduling for Automated Guided Vehicles and Unmanned Container Trucks at Automated Container Terminals Considering Conflicts

Port development is a critical component in constructing a resilient transportation infrastructure. The burgeoning integration of automated guided vehicles (AGVs) within container terminals, in conjunction with the orchestrated scheduling of unmanned container trucks (UCTs), is essential for the sustainable expansion of port operations in the future. This study examined the influence of AGVs in automated container terminals and the synergistic scheduling of UCTs on port operations. Comparative experiments were meticulously designed to evaluate the feasibility of integrated scheduling schemes. Through the development of optimization models that consider conflict-free paths for both AGVs and UCTs, as well as strategies for conflict resolution, a thorough analysis was performed. Advanced genetic algorithms were engineered to address task-dispatching models. In contrast, the A* optimization search algorithm was adapted to devise conflict-free and conflict-resolution paths for the two vehicle types. A range of scaled scenarios was utilized to assess the impact of AGVs and UCTs on the joint-scheduling process across various configuration ratios. The effectiveness of the strategies was appraised by comparing the resultant path outcomes. Additionally, comparative algorithmic experiments were executed to substantiate the adaptability, efficacy, and computational efficiency of the algorithms in relation to the models. The experimental results highlight the viability of tackling the joint-scheduling challenge presented by AGVs and UCTs in automated container terminals. When juxtaposed with alternative scheduling paradigms that operate independently, this integrated approach exhibits superior performance in optimizing the total operational costs. Consequently, it provides significant insights into enhancing port scheduling practices.

Improved random forest model for smoke detection and early warning based on sparrow search algorithm

Smoke detection technology is of great significance in the field of fire safety, aiming at the timely detection of smoke released from fires or other combustion events to safeguard people's lives and properties. In this paper, a smart smoke alarm device is developed based on AI algorithm using UCL open source dataset, which is processed by AI recognition after collecting data through IoT to achieve accurate judgement of smoke situation. The dataset is divided by 6:4 ratio, and the training set confusion matrix shows that all 1159 smoke alarm tests are correctly predicted, of which 445 times predict no smoke and 714 times predict smoke, with an accuracy rate of 100%. The test set confusion matrix shows that only 1 out of 1158 tests was incorrectly predicted, of which 184 predicted no smoke, 312 predicted smoke, and 1 was misjudged as no smoke even though it should have been smoke, with an accuracy rate of 99.7%. The successful application of this technology provides a reliable guarantee for fire warning and demonstrates the great potential of AI in the field of security.

GSearch: ultra-fast and scalable genome search by combining K-mer hashing with hierarchical navigable small world graphs.

Genome search and/or classification typically involves finding the best-match database (reference) genomes and has become increasingly challenging due to the growing number of available database genomes and the fact that traditional methods do not scale well with large databases. By combining k-mer hashing-based probabilistic data structures (i.e. ProbMinHash, SuperMinHash, Densified MinHash and SetSketch) to estimate genomic distance, with a graph based nearest neighbor search algorithm (Hierarchical Navigable Small World Graphs, or HNSW), we created a new data structure and developed an associated computer program, GSearch, that is orders of magnitude faster than alternative tools while maintaining high accuracy and low memory usage. For example, GSearch can search 8000 query genomes against all available microbial or viral genomes for their best matches (n= ∼318 000 or ∼3 000 000, respectively) within a few minutes on a personal laptop, using ∼6 GB of memory (2.5 GB via SetSketch). Notably, GSearch has an O(log(N)) time complexity and will scale well with billions of genomes based on a database splitting strategy. Further, GSearch implements a three-step search strategy depending on the degree of novelty of the query genomes to maximize specificity and sensitivity. Therefore, GSearch solves a major bottleneck of microbiome studies that require genome search and/or classification.

Robust Fast Fracture Plane Orientation Angle Search Algorithm for Puck 3D Inter-Fibre Failure Criterion

In the present work, a novel fast fracture plane orientation angle (FPOA) search algorithm for the 3D Puck failure criterion is proposed. In the 3D Puck failure criterion, a linear search algorithm is employed to calculate the maximum inter-fibre failure (IFF) value by iterating and comparing the IFF value for each FPOA. This process itself requires a substantial amount of computational resources. The proposed fast FPOA search algorithm is implemented to substitute the linear search algorithm in order to reduce the computational time. A total of 1×105 randomised stress cases are used to analyse the accuracy of the algorithm. The result was then compared with the Puck Stepwise Seach Method (SSM) and other fast FPOA search algorithms. The results show that the proposed fast FPOA search algorithm has better accuracy compared to the other fast FPOA search algorithms and is almost 5 times faster compared to the SSM algorithm by Puck. In addition, a subroutine contains the Puck failure criterion and the proposed fast FPOA search algorithm is embedded into a Finite Element Analysis (FEA) software to simulate the open-hole test (OHT) experiment on the composite material.

Fault Diagnosis Method of Bearings Based on SCSSA-VMD-MCKD

To tackle the issue of detecting early, subtle faults in rolling bearings in the presence of noise interference, the SCSSA-VMD-MCKD method is suggested. This method optimizes the Variational Mode Decomposition (VMD) and Maximum Correlated Kurtosis Deconvolution (MCKD) by integrating the sine-cosine and Cauchy Mutation Sparrow Search Algorithm (SCSSA). The approach aims to effectively diagnose faults in rolling bearings by leveraging the strengths of VMD and MCKD in noise reduction and highlighting fault frequencies. The method utilizes the SCSSA algorithm to autonomously search for parameters in both VMD and MCKD, using the EnvelopeCrest Factor Ec as a fitness function. Firstly, SCSSA is employed to optimize the decomposition mode number K and penalty factor α in the VMD algorithm. Secondly, the parameters in the MCKD algorithm are optimized, and MCKD is used for deconvolution to enhance the impulsive characteristics of the best modal component. Finally, the signal is further analyzed after deconvolution. The results demonstrate that this algorithm can effectively identify subtle fault signals in bearing signals and diagnose fault frequencies in noisy environments. The accuracy of fault diagnosis can reach nearly 99%.

Temperature compensation of a hybrid algorithm optimized neural network: Application to a 64-channel electronic pressure scanner

As a high-precision instrument, electronic pressure scanners have a crucial role in multi-point pressure measurements. However, the internal piezoresistive components drift with temperature, and this phenomenon has become a key factor restricting the accuracy. In this study, a hybrid algorithm based on back propagation neural network (BPNN) with particle swarm optimization (PSO) and gravity search algorithm (GSA) is reported for multi-channel temperature compensation to solve this problem Fundamentally, the global optimization search capability of GSA and the fast local convergence advantage of PSO are utilized so that the model parameters of the BPNN are guaranteed to be reliable and the efficiency of its execution is further improved. To this end, the temperature data between −40 °C and 70 °C obtained through the calibration experimental system are analyzed for compensation of the electronic pressure scanner from the 0 to 700 kPa adiabatic range. The results show that, compared with BP neural network, radial basis function (RBF) neural network, and PSO-BP method, the new PSOGSA-BP approach has higher accuracy in pressure sensor temperature compensation, in which the absolute error is only 0.2301 kPa and the full-scale error is 0.03% full scale. In addition, the method can be applied to electronic pressure scanners with stronger generalization and demonstrated to be suitable for temperature compensation.

Retraction Note: An Improved dynamic self-adaption cuckoo search algorithm based on collaboration between subpopulations

Retraction Note: An Improved dynamic self-adaption cuckoo search algorithm based on collaboration between subpopulations

Intelligent toy tracking trajectory design based on mobile cloud terminal deployment and depth-first search algorithm

The popularization of intelligent toys enriches the lives of the general public. To provide the public with a better toy experience, we propose the intelligent toy tracking method by the mobile cloud terminal deployment and depth-first search algorithm. Firstly, we construct a toy detection model via Transformer, which realizes the positioning of toys in the image through the refined region adaptive boundary representation. Then, using these detected continuous frames, we improve the toy tracking based on a depth-first search. Long-short-term memory (LSTM) constructs the continuous frame tracking structure, and the depth-first search mechanism is embedded to realize the accurate tracking of multiple targets in continuous frames. Finally, to realize the terminal marginalization of the proposed method, this chapter proposes a lightweight model deployment method based on mobile cloud terminals to realize the maintenance of the optimal machine state of intelligent toys. The experiment proves that our proposed target method can reach the world-leading level and obtain the mAP value of 0.858. Our tracking method can also perform excellently with a MOTA value of 0.916.

Digital Educational Tools for Undergraduate Nursing Education: A Review of Serious Games, Gamified Applications and Non-Gamified Virtual Reality Simulations/Tools for Nursing Students

Educational technology has advanced tremendously in recent years, with several major developments becoming available in healthcare professionals’ education, including nursing. Furthermore, the COVID-19 pandemic resulted in obligatory physical distancing, which forced an accelerated digital transformation of teaching tools. This review aimed to summarize all the available digital tools for nursing undergraduate education developed from 2019 to 2023. A robust search algorithm was implemented in the Scopus database, resulting in 1592 publications. Overall, 266 relevant studies were identified enrolling more than 22,500 undergraduate nursing students. Upon excluding multiple publications on the same digital tool, studies were categorized into three broad groups: serious games (28.0%), gamified applications (34.5%), and VR simulations and other non-gamified digital interventions (37.5%). Digital tools’ learning activity type (categories = 8), geographical distribution (countries = 34), educational subjects (themes = 12), and inclusion within a curriculum course (n = 108), were also explored. Findings indicate that digital educational tools are an emerging field identified as a potential pedagogical strategy aiming to transform nursing education. This review highlights the latest advances in the field, providing useful insights that could inspire countries and universities which have not yet incorporated digital educational tools in their nursing curriculum, to invest in their implementation.

A Local Search Algorithm with Vertex Weighting Strategy and Two-Level Configuration Checking for the Minimum Connected Dominating Set Problem.

The minimum connected dominating set problem is a combinatorial optimization problem with a wide range of applications in many fields. We propose an efficient local search algorithm to solve this problem. In this work, first, we adopt a new initial solution construction method based on three simplification rules. This method can reduce the size of the original graph and thus obtain a high-quality initial solution. Second, we propose an approach based on a two-level configuration checking strategy and a tabu strategy to reduce the cycling problem. Third, we introduce a perturbation strategy and a vertex weighting strategy to help the algorithm be able to jump out of the local optimum effectively. Fourth, we combine the scoring functions Cscore and Mscore with the aforementioned strategies to propose effective methods for selecting vertices. These methods assist the algorithm in selecting vertices that are suitable for addition to or removal from the current candidate solution. Finally, we verify the performance advantages of the local search algorithm by comparing it with existing optimal heuristic algorithms on two sets of instances. The experimental results show that the algorithm exhibits better performance on two sets of classical instances.