Search Algorithm Research Articles

Extending boids for safety-critical search and rescue

Robot swarms can accomplish complex tasks, and in this work, we seek to design swarm robotic algorithms for search and rescue that are scalable to large swarms, efficient in terms of computations, safe from collisions, and tunable to mediate the trade-off between exploration and exploitation in the search. We propose extending the Boids algorithm to accomplish this. Without modifying the three Boids rules of alignment, cohesion, and separation, we add target-seeking and general collision avoidance by using ghost boids. Additionally, we use a control barrier function to improve safety at the cost of increased computation. Via simulation in a search and rescue task, we analyze the trade-offs between safety, computational efficiency, and coverage of the environment for our algorithm.

Clutaxis: an information-driven source search method balancing exploration and exploitation in turbulent environments

Locating unknown emission sources in turbulent environments is a challenging yet crucial task, particularly in emergency response scenarios. Existing studies have developed information-theoretic approaches to fuse intermittent information collected by mobile sensors regarding the sources. This fused information is then used to support source-term estimation (STE) in various search algorithms. Among these, the cognitive strategy—a promising information-driven search algorithm—leverages a reward-based action selection mechanism to balance exploration and exploitation during each search step. However, this mechanism is hampered by a high computational load and rigid search trajectories, limiting its application in real-world systems. To address these issues, this paper proposes a novel information-driven search method called Clutaxis, based on a global exploration and exploitation tradeoff principle. Specifically, a particle filter is leveraged to maintain the STE. After projecting the particle filter samples onto a 2D search scene, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to extract the density information of the samples, which is then used to construct a belief source area (BSA). By leveraging the uncertainty of the BSA, Clutaxis adopts explorative or exploitative actions with no restrictions on motion direction. Through dedicated simulations, the experimental results demonstrate the robustness of Clutaxis to key parameters and its advantages in computational complexity and search performance compared to two state-of-the-art algorithms (Infotaxis and Entrotaxis) and two Clutaxis variants (Clutaxis_ER and Clutaxis_EI).

Battery Remaining Useful Life Estimation based on Particle Swarm Optimization-Neural Network

Determining the Remaining Useful Life (RUL) of a battery is essential for several purposes, including proactive maintenance planning, optimizing resource allocation, preventing unforeseen failures, improving safety, extending battery lifespan, and achieving accurate cost savings. Concerning that matter, this study proposed hybrid Particle Swarm Optimization–Neural Network (PSO-NN) for estimating battery RUL. In the evaluation of the proposed method, the effectiveness is assessed using the metrics of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The dataset employed for this investigation comprises eight input parameters and one output variable, representing the battery RUL. In conducting an analysis, the performance of the PSO-NN model is compared with hybrid NN with Cultural Algorithm (CA-NN) and Harmony Search Algorithm (HSA-NN), as well as the standalone Autoregressive Integrated Moving Average (ARIMA). Upon examination of the findings, it becomes evident that the PSO-NN model outperforms the alternatives with an MAE of 2.7708 and an RMSE of 4.3468, significantly lower than HSA-NN (MAE: 22.0583, RMSE: 34.5154), CA-NN (MAE: 9.1189, RMSE: 22.4646), and ARIMA (MAE: 494.6275, RMSE: 584.3098). The PSO-NN also achieves the lowest maximum error of 104.7381 compared to 490.3125 for HSA-NN, 827.0163 for CA-NN, and 1,160.0000 for ARIMA. Additionally, the low two-tail probability values (P(T≤t)), all below the significance level of 0.05, indicate that the differences between PSO-NN and the other methods (HSA-NN, CA-NN, and ARIMA) are statistically significant. These results highlight the superior accuracy and robustness of the PSO-NN model in predicting battery RUL. This study contributes to the field by presenting the PSO-NN as a highly effective tool for accurate battery RUL estimation, as evidenced by its superior performance over alternative methods.

PP-NAS: Searching for Plug-and-Play Blocks on Convolutional Neural Networks.

Multiscale features are of great importance in modern convolutional neural networks, showing consistent performance gains on numerous vision tasks. Therefore, many plug-and-play blocks are introduced to upgrade existing convolutional neural networks for stronger multiscale representation ability. However, the design of plug-and-play blocks is getting more and more complex, and these manually designed blocks are not optimal. In this work, we propose PP-NAS to develop plug-and-play blocks based on neural architecture search (NAS). Specifically, we design a new search space PPConv and develop a search algorithm consisting of one-level optimization, zero-one loss, and connection existence loss. PP-NAS minimizes the optimization gap between super-net and subarchitectures and can achieve good performance even without retraining. Extensive experiments on image classification, object detection, and semantic segmentation verify the superiority of PP-NAS over state-of-the-art CNNs (e.g., ResNet, ResNeXt, and Res2Net). Our code is available at https://github.com/ainieli/PP-NAS.

Machine Learning Surrogate Model Optimized by Improved Sparrow Search Algorithm for Multi-Objective Optimization of Permanent Magnet Synchronous Motor Direct-Drive Pump

Machine Learning Surrogate Model Optimized by Improved Sparrow Search Algorithm for Multi-Objective Optimization of Permanent Magnet Synchronous Motor Direct-Drive Pump

Secret image restoration with interpolation and social network search

Image restoration is an essential task in image processing. Due to the secret image extracted from the corrupted stego-image and its pixels may suffer different levels of damage, the type of noise is particular. Most of the existing methods have a challenge in directly removing this type of noise. Hence, we propose a corrupted secret image restoration algorithm with interpolation and social network search. Firstly, a corrupted secret image is divided into several blocks, and the convex hull of each block is calculated. For the corrupted pixels in the convex hull, the natural neighbor interpolation and bi-harmonic spline interpolation are utilized to generate the estimation values of corrupted pixels according to the density of the trusted pixels. For the corrupted pixels outside the convex hull, the social network search algorithm is applied to generate the estimated values of corrupted pixels. Finally, all corrupted pixels are recovered by the ones of their candidate values closest to their estimated values. The performance of the various methodologies was evaluated using the USCSIPI database, employing peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) indices, which demonstrated an enhancement of more than 0.78 dB and 0.0431, respectively. Experiments show that our algorithm outperforms other state-of-the-art (SOTA) algorithms.

Influence of heating elements layout on temperature uniformity in a large size heat treatment furnace

Computational Fluid Dynamics simulations were used to optimize heating elements placement on the walls of a large size electrical heat treatment furnace in order to reduce temperature gradients within the furnace and uneven temperatures on different faces of the blocks which could result in variabilities in final mechanical properties. Initial evaluations of different layouts highlighted the effectiveness of equipping two side walls. Subsequently, an optimal percentage of side wall coverage was identified through simulations and multi-objective evolutionary optimization. Genetic and pareto search algorithms yielded 10 % and 14.2 % as optimum values, respectively. Implementing an optimal coverage demonstrated significant reductions in surface temperature non-uniformity, surface-to-center temperature differential, and temperature differences between loaded blocks. These improvements suggest a potential for enhanced uniformity in mechanical properties across a batch of products. This approach presents a promising strategy for obtaining consistent and efficient heat treatment cycles in industrial heat treatment furnaces resulting in energy saving and improved product quality.

TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction.

Autonomous driving systems are a rapidly evolving technology. Trajectory prediction is a critical component of autonomous driving systems that enables safe navigation by anticipating the movement of surrounding objects. Lidar point-cloud data provide a 3D view of solid objects surrounding the ego-vehicle. Hence, trajectory prediction using Lidar point-cloud data performs better than 2D RGB cameras due to providing the distance between the target object and the ego-vehicle. However, processing point-cloud data is a costly and complicated process, and state-of-the-art 3D trajectory predictions using point-cloud data suffer from slow and erroneous predictions. State-of-the-art trajectory prediction approaches suffer from handcrafted and inefficient architectures, which can lead to low accuracy and suboptimal inference times. Neural architecture search (NAS) is a method proposed to optimize neural network models by using search algorithms to redesign architectures based on their performance and runtime. This paper introduces TrajectoryNAS, a novel neural architecture search (NAS) method designed to develop an efficient and more accurate LiDAR-based trajectory prediction model for predicting the trajectories of objects surrounding the ego vehicle. TrajectoryNAS systematically optimizes the architecture of an end-to-end trajectory prediction algorithm, incorporating all stacked components that are prerequisites for trajectory prediction, including object detection and object tracking, using metaheuristic algorithms. This approach addresses the neural architecture designs in each component of trajectory prediction, considering accuracy loss and the associated overhead latency. Our method introduces a novel multi-objective energy function that integrates accuracy and efficiency metrics, enabling the creation of a model that significantly outperforms existing approaches. Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field. Experimental results reveal that TrajcetoryNAS yields a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset.

Optimal nonlinear Fractional-Order Proportional-Integral-Derivative controller design using a novel hybrid atom search optimization for nonlinear Continuously stirred Tank reactor

Atom search optimization (ASO) algorithm derived from physics molecular dynamics. Lennard-Jones(L-J) and bond length potential of molecules are used to derive the model for optimization. In this paper, ASO is used for developing a nonlinear Fractional Order Proportional Integral Derivative controller (NL-FOPID) for Continuously Stirred Tank Reactor (CSTR). The convergence characteristics of ASO was improved by proposing a novel hybridization approach. The proposed hybridization approach called Hybrid ASO(HASO) guides the Atom search algorithm to optimally replace the atoms that goes out of the boundary of the search space. The designed algorithm is implemented to optimize various unimodal and multi model standard benchmark functions. From results obtained from this extensive simulation, it is indicated that proposed approach increased the convergence rate and also improved the optimization effort of conventional ASO. The proposed algorithm also tested with controller design for nonlinear CSTR. The NLPID and NL FOPID designed by HASO was better than conventional controllers found in the literature.

A comprehensive comparison of advanced metaheuristic photovoltaic maximum power tracking algorithms during dynamic and static environmental conditions

This study introduces a novel technique for achieving the global peak (GP) in solar photovoltaic (PV) systems under partial shadowing conditions (PSC) using the Dandelion Optimizer Algorithm (DOA), inspired by the dispersal of dandelion seeds in the wind. The proposed approach aims to enhance the power generation efficiency of PV systems across various scenarios, including dynamic uniform, dynamic PSCs, static uniform irradiances, and static PSCs. The proposed approach improves tracking efficiency, provides non-oscillatory steady-state responses, and reduces transients as well as enhancing the dynamic performance of the whole system. Simulation and hardware-in-loop (HIL) experiments demonstrate that the DOA outperforms several state-of-the-art techniques, such as hybrid grey wolf optimizer since-cosine algorithm (HGWOSCA), grasshopper optimization algorithm (GOA), dragonfly optimizer (DFO), particle swarm optimizer with gravitational search (PSOGS), PSO, cuckoo search algorithm (CSA), perturb &observe (P&O), and incremental conductance (INC), achieving average efficiencies of 99.93 %, 88.84 %, 94.48 %, 87.12 %, 88.05 %, 94.79 %, 93.82 %, 85.25 %, and 77.93 %, respectively. These results underscore the DOA's effectiveness in improving maximum power point tracking (MPPT) performance in solar arrays, particularly under challenging dynamic PSC conditions.

A Novel Survey on Comparison of Blackhole Attack Detection Techniques

Black hole attacks occur when a hacker takes control of a collection of nodes, modifies their programming, and blocks packets from being forwarded to the base station. This results in data being grabbed and traveling to the attacker's (black hole) area. By separating the network, these attacks are easy to develop and have the potential to reduce system efficiency, preventing base stations from receiving crucial information. Blackhole nodes can cause parameters of network performance such as throughput and end-to-end delay to change, with throughput falling and end-to-end delay rising. This paper highlights the efficiency and limitations with a comparative analysis of all existing approaches. This paper illustrated results using the AODV protocol for black hole attack detection using the Cuckoo search algorithm. The network is created for 15 nodes using a network simulator. Keyword: Blackhole, Attack, Security, Network-Simulator, Detection.

Shape and roundness accuracy prediction of the roll grinding based on improved Elman neural network

Abstract Due to the strong nonlinear relationship between the shape and roundness of the roller and the grinding process parameters, a prediction method for roll shape error and roundness error based on an improved Elman neural network is proposed. During the training process of the Elman neural network, Sine chaotic mapping is used to make the population distribution more uniform, and the sparrow search algorithm is used to obtain the optimal parameters of the Elman neural network. The verification results show that the prediction errors of the roll shape and roundness are both less than 10%, and the accuracy of the prediction model is higher than 85%, which proves the effectiveness of the proposed method.

A Targeted Search for Variable Gravitationally Lensed Quasars

We present a pipeline to identify photometric variability within strong gravitationally lensing candidates, in the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys. In our first paper, we laid out our pipeline and presented seven new gravitationally lensed supernovae candidates in a retrospective search. In this companion paper, we apply a modified version of that pipeline to search for gravitationally lensed quasars. From a sample of 5807 strong lenses, we have identified 13 new gravitationally lensed quasar candidates (three of them quadruply lensed). We note that our methodology differs from most lensed quasar search algorithms that solely rely on the morphology, location, and color of the candidate systems. By also accounting for the temporal photometric variability of the posited lensed images in our search via difference imaging, we have discovered new lensed quasar candidates. While variability searches using difference imaging algorithms have been done in the past, they are typically performed over vast swathes of the sky, whereas we specifically target strong gravitationally lensed candidates. We also have applied our pipeline to 655 known gravitationally lensed quasar candidates from past lensed quasar searches, of which we identified 13 that display significant variability (one of them quadruply lensed). This pipeline demonstrates a promising search strategy to discover gravitationally lensed quasars in other existing and upcoming surveys.

O19 CAUSAL INFERENCE FOR HYPERTENSION DETECTION WITH WEARABLE DEVICES

Background and Objective: Hypertension is a leading cause of cardiovascular disease and premature death worldwide and it puts a heavy burden on the healthcare system. It is, therefore, very important to detect and evaluate hypertension and related cardiovascular events so as for early prevention, detection and management. Hypertension can be evaluated in real time with wearable noninvasive cardiac signals, such as electro-cardiogram (ECG) and photoplethysmography (PPG). Most previous studies predicted hypertension from ECG and PPG signals with extracted features that are correlated with hypertension. However, correlation is sometimes unreliable and may be affected by confounding factors. In this study, we explored the feasibility of predicting the risk of hypertension using causal inference methods. Additionally, we paid special attention to and verified the reliability and effectiveness of causality compared to correlation. Methods: Firstly, we constructed causal graphs by the Greedy Equivalence Search algorithm, and then applied causal strategies to obtain the optimal causal graph. Finally, we used machine learning classification algorithms to achieve hypertension prediction. Results: The machine learning classification models achieve great classification performance, with accuracy being 0.89, precision being 0.92, recall being 0.82, and F1-score being 0.87, which outperformed the correlation-based hypertension detection. Conclusions: The results indicate that the causal inference-based approach can potentially clarify the mechanism of hypertension detection with noninvasive signal and effectively detect hypertension. In addition, the results also re-veal that causality is more reliable and effective compared to correlation.

Application of LSTM neural network in the research of cyclical industries: taking the automotive industry as an illustrative case

Anticipating future developmental trends within an industry stands as one of the paramount responsibilities for industry researchers. Nevertheless, owing to the intricate and subjective nature inherent in industry research, coupled with researchers usually having to rely on their own subjective judgments when making predictions, the forecasting outcomes frequently prove unsatisfactory. In order to address this issue, this study introduces deep learning technology into the industry research field and employs Long & Short-Term Memory (LSTM) networks to forecast a typical cyclical industry - the automotive industry. In the specific experiment, Adam optimization algorithm is employed to enhance model training speed, followed by utilization of the grid search algorithm for optimal parameter selection. Subsequently, the obtained optimal model is utilized for LSTM multivariate sequence prediction in order to forecast the future development trend of the automotive industry over the next six months. The ultimate outcome demonstrates that the LSTM model accurately anticipated the forthcoming U-shaped rebound trend in the automotive industry, albeit with some limitations in predicting precise numerical values and timing. Nevertheless, it aligns closely with the actual developmental trajectory. This investigation represents a constructive application of deep learning technology in industrial research, and its final findings hold significant implications for future industry research endeavors.

Scheduling of memory chips for final testing on parallel machines considering power constraints and deteriorating effects

This paper delves into the intricate scheduling strategies crucial for the final testing phase of memory chip manufacturing within the semiconductor industry and other related sectors. It specifically addresses the complex serial-batching scheduling problem, where memory chips are tested on parallel machines under power constraints and chip deterioration effects. The processing time for each chip is significantly influenced by both the cumulative processing time of preceding chips and the power requirements for testing. We formulate this real-world optimization problem using a mixed integer programming model. Exact solutions for small instances are obtained using a commercial solver. However, due to the model's complexity, we also develop heuristic solutions to efficiently handle larger instances. Based on the derivation of structural properties, we develop two tailored heuristic algorithms to determine the schedule for the final testing of memory chips. Additionally, we propose a refined Variable Neighborhood Search algorithm (VNS-H) that seamlessly integrates five local search strategies with two supplementary heuristic algorithms, dynamically alternating between them to ensure a balance between computational efficiency and the quality of the solutions obtained. Additionally, we establish a lower bound to validate the effectiveness of these solutions, particularly for large-scale instances. To validate the efficacy and robustness of our proposed metaheuristic algorithm, we conduct a rigorous comparison of the VNS-H algorithm with five other metaheuristic algorithms that have promising performance in various optimization problems. The results highlight the superior performance of the VNS-H algorithm. In small-scale instances, our VNS-H algorithm achieves an average makespan that is 5.52% lower compared to the original VNS. For large-scale instances, the VNS-H algorithm reduces the average makespan by 13.46% compared to VNS. Finally, we discuss the managerial implications of our findings, providing insights specifically tailored to semiconductor manufacturing enterprises based on the outcomes of this study.

Collaborative train timetabling and passenger flow control in oversaturated metro lines considering state-dependence

Congestion downgrades the moving speed of passengers in trains and platforms. This in turn affects congestion. That is, the moving speed of passengers interacts with congestion. It is state-dependent and obeys the fundamental diagram. This interaction makes the system unstable and can result in substantial passenger delays and heightened security risks in oversaturated metro lines. This study aims to rectify this by developing a more realistic collaborative optimization approach. Firstly, we formulate a deterministic fluid queuing network model to accurately capture the dynamic propagation of congestion in oversaturated metro lines. This model incorporates the fundamental diagram, thereby effectively considering the interaction between passenger movement speeds and congestion. A recursive algorithm is devised to solve the fluid queuing network model. The complexity of the algorithm is independent of the capacities of the stations and trains. This provides the possibility of analyzing large-scale metro networks. Based on the queuing network model, a mixed-integer nonlinear programming model aimed at minimizing the total passenger waiting time is built. To solve the model effectively, we employ an alternating direction method of multipliers framework to decompose the model into nonlinear programming and integer nonlinear programming problems, which are respectively solved by improved simultaneous perturbation stochastic approximation and adaptive large neighborhood search algorithms. Finally, a small-scale case and a real-world case with data from Chengdu metro lines are implemented to demonstrate the performance and effectiveness of the proposed approach. The experimental results provide some interesting insights. (1) Without consideration of state-dependence, the queuing model underestimates the passenger waiting time and overestimates the service levels. (2) As demand increases, excluding extreme oversaturation scenarios, the optimized results of the state-independent model progressively deteriorate compared to our state-dependent model, with the largest percentage decline being 50.23%. This is especially noticeable when the train capacity is limited. (3) The contribution ratio of passenger flow control in mitigating congestion gradually surpasses that of train timetabling with increasing demand. In the small-scale case, the former contribution ratio to optimization rises from negligible to 98.19%.

Quantum-Accelerated Flight Selection: Probing Grover's Algorithm and Quantum Device Efficiency

Current flight search platforms primarily consider four essential factors when planning a trip: departure/arrival dates, as well as the origin and destination locations. However, when additional parameters are added to this search, the problem shifts from a simple to a complex search, as the engine must sift through a massive dataset of flights, including information on airlines, flight routes, fees, and more. To address this challenge and improve flight search efficiency amidst data-intensive and resource-demanding environments, this paper proposes the use of Grover's search algorithm. This algorithm is demonstrated as the optimal solution for searches with increased constraints. The paper highlights the practical application of Grover's search algorithm across three datasets of assorted sizes, as well as various quantum hardware and simulators available in the NISQ era. Furthermore, this paper provides an in-depth understanding of the complexity of quantum circuit design, including the key phases of state encoding and amplitude amplification. The effectiveness of these approaches is evaluated through analysis of execution times and quantum measurement results. The aim is to showcase the potential of quantum computing in revolutionizing real-world search tasks, particularly in the realm of flight selection.

Optimal location and sizing of renewable distributed generations and electric vehicle charging stations

Many countries are aiming to replace gasoline-based vehicles with electric vehicles (EVs). The increasing adoption of EVs has led to a surge in the number of charging stations, significantly impacting the electrical grid with issues such as power quality degradation, increased losses, and voltage fluctuations. In response to these challenges, there is a growing interest in integrating distributed generation from unconventional and renewable sources into the grid to power EV Charging Stations (EVCSs). However, this integration poses new complexities, including increased power losses and voltage instability. Consequently, the optimal allocation and sizing of Renewable Distributed Generations (RDGs) and EVCSs have become critical planning considerations. This paper addresses these issues by formulating a multi-objective optimization problem aimed at minimizing power losses and improving the voltage profile of distribution systems. The study introduces several constraints, including the placement of EVCSs and RDGs at separate buses, and employs particle swarm optimization and cuckoo search algorithm to simultaneously determine the optimal locations and sizes of the RDGs and the optimal locations of the EVCSs. The effectiveness of the proposed techniques is demonstrated through simulations on IEEE 33 radial and meshed distribution systems, illustrating their capability to identify optimal configurations for integrating RDGs and EVCSs.

Advanced thermal prediction for green roofs: CNN-LSTM model with SSA optimization

Green roofs serve as an innovative strategy for urban architecture, playing a pivotal role in mitigating urban heat island effects and enhancing building energy efficiency. This study proposes a hybrid model based on the Sparrow Search Algorithm (SSA) optimized Convolutional Neural Network − Long Short-Term Memory Network (CNN-LSTM) for rapid and accurate prediction of the thermal performance of green roofs. The model integrates the spatial feature extraction of CNN with the temporal analysis capability of LSTM, enhancing the predictive accuracy of multi-feature time series through global optimization by SSA. Taking the green roof in Guangzhou China as a case study and utilizing hourly meteorological and thermal property data, this model has demonstrated superior predictive performance: the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-squared (R2), and Mean Absolute Percentage Error (MAPE) for the prediction of inner surface temperature are 0.718, 0.908 %, 0.922, and 0.025 %, respectively; for the outer surface temperature prediction, the corresponding values are 0.618, 0.886 %, 0.983, and 0.028 %. Comparative analysis with existing AI models and ablation experiments further confirm the superiority of this model. Additionally, through Spearman correlation analysis, this study reveals the correlation between key input parameters and surface temperatures, providing data support for the effective design and management of green roofs. The model presented in this study not only provides an efficient tool for predicting the thermal performance of green roofs but also holds significant importance for promoting green building and sustainable urban development.