Greedy Search Strategy Research Articles

Dynamic stacking ensemble for cross-language code smell detection.

Code smells refer to poor design and implementation choices by software engineers that might affect the overall software quality. Code smells detection using machine learning models has become a popular area to build effective models that are capable of detecting different code smells in multiple programming languages. However, the process of building of such effective models has not reached a state of stability, and most of the existing research focuses on Java code smells detection. The main objective of this article is to propose dynamic ensembles using two strategies, namely greedy search and backward elimination, which are capable of accurately detecting code smells in two programming languages (i.e., Java and Python), and which are less complex than full stacking ensembles. The detection performance of dynamic ensembles were investigated within the context of four Java and two Python code smells. The greedy search and backward elimination strategies yielded different base models lists to build dynamic ensembles. In comparison to full stacking ensembles, dynamic ensembles yielded less complex models when they were used to detect most of the investigated Java and Python code smells, with the backward elimination strategy resulting in less complex models. Dynamic ensembles were able to perform comparably against full stacking ensembles with no significant detection loss. This article concludes that dynamic stacking ensembles were able to facilitate the effective and stable detection performance of Java and Python code smells over all base models and with less complexity than full stacking ensembles.

An improved variable neighborhood search algorithm embedded temporal and spatial synchronization for vehicle and drone cooperative routing problem with pre-reconnaissance

Drones are increasingly utilized for transportation reconnaissance due to their expansive field of view, cost-effectiveness, and agility. They can pre-reconnaissance the traveling routes of the ground vehicles carrying valuable goods to ensure the safety of vehicles and their goods. This rises a novel routing problem for the Vehicles and their Pre-Reconnaissance Drones, which is an integrated point and arc routing problem with temporal and spatial synchronization. A mixed integer linear programming model is developed to formulate the problem with complex synchronization constraints. The Variable Neighborhood Search algorithm integrated with the Temporal & Spatial synchronization-based Greedy search and simulated annealing strategy is designed to solve the model. A practical case based on real urban data from Beijing, China, and random instances with different sizes are tested and compared with the proposed algorithm. Computational results indicated that the proposed algorithm can solve the problem efficiently and outperform the simulated annealing algorithm and the greedy algorithm.

Efficient and Intelligent Feature Selection via Maximum Conditional Mutual Information for Microarray Data

The challenge of analyzing microarray datasets is significantly compounded by the curse of dimensionality and the complexity of feature interactions. Addressing this, we propose a novel feature selection algorithm based on maximum conditional mutual information (MCMI) to identify a minimal feature subset that is maximally relevant and non-redundant. This algorithm leverages a greedy search strategy, prioritizing both feature quality and classification performance. Experimental results on high-dimensional microarray datasets demonstrate our algorithm’s superior ability to reduce dimensionality, eliminate redundancy, and enhance classification accuracy. Compared to existing filter feature selection methods, our approach exhibits higher adaptability and intelligence.

The SVD-enhanced bees algorithm, a novel procedure for point cloud registration

Point cloud registration entails the estimation of the rigid transformation which best aligns two point sets. At present, Iterative Closest Point is arguably the best-known and one of the most effective algorithms for point cloud registration. Iterative Closest Point uses singular value decomposition to optimise the alignment of two point sets via least squares fit. However, the algorithm is liable to converge to sub-optimal solutions due to its greedy search strategy. In this study, the problem of point cloud registration is addressed using the popular Bees Algorithm metaheuristic. Thanks to its global search strategy, the Bees Algorithm is known to be impervious to sub-optimal convergence. Singular value decomposition is used as a problem-specific operator within the Bees Algorithm to improve the efficiency of the search. Experimental tests showed that the standard Bees Algorithm outperformed Iterative Closest Point, an Evolutionary Algorithm, and Particle Swarm Optimisation in terms of consistency and precision of the point cloud registration results. The Bees Algorithm also excelled for its robustness to noisy data. When enhanced by singular value decomposition, the Bees Algorithm outperformed the standard Bees Algorithm, and similarly enhanced evolutionary and particle swarm optimisers. The running times of the proposed method were compatible with online implementation. It is concluded that the proposed combination of bees-inspired optimisation and least squares fitting allows accurate, efficient, and consistent registration of point cloud models.

Exploiting Instance-level Relationships in Weakly Supervised Text-to-Video Retrieval

Text-to-Video Retrieval is a typical cross-modal retrieval task that has been studied extensively under a conventional supervised setting. Recently, some works have sought to extend the problem to a weakly supervised formulation, which can be more consistent with real-life scenarios and more efficient in annotation cost. In this context, a new task called Partially Relevant Video Retrieval (PRVR) is proposed, which aims to retrieve videos that are partially relevant to a given textual query, i.e., the videos containing at least one semantically relevant moment. Formulating the task as a Multiple Instance Learning (MIL) ranking problem, prior arts rely on heuristics algorithms such as a simple greedy search strategy and deal with each query independently. Although these early explorations have achieved decent performance, they may not fully utilize the bag-level label and only consider the local optimum, which could result in suboptimal solutions and inferior final retrieval performance. To address this problem, in this paper, we propose to exploit the relationships between instances to boost retrieval performance. Based on this idea, we creatively put forward: 1) a new matching scheme for pairing queries and their related moments in the video; 2) a new loss function to facilitate cross-modal alignment between two views of an instance. Extensive validations on three publicly available datasets have demonstrated the effectiveness of our solution and verified our hypothesis that modeling instance-level relationships is beneficial in the MIL ranking setting. Our code will be publicly available at https://github.com/xjtupanda/BGM-Net.

Dealing with the Data Imbalance Problem in Pulsar Candidate Sifting Based on Feature Selection

Pulsar detection has become an active research topic in radio astronomy recently. One of the essential procedures for pulsar detection is pulsar candidate sifting (PCS), a procedure for identifying potential pulsar signals in a survey. However, pulsar candidates are always class-imbalanced, as most candidates are non-pulsars such as RFI and only a tiny part of them are from real pulsars. Class imbalance can greatly affect the performance of machine learning (ML) models, resulting in a heavy cost as some real pulsars are misjudged. To deal with the problem, techniques of choosing relevant features to discriminate pulsars from non-pulsars are focused on, which is known as feature selection. Feature selection is a process of selecting a subset of the most relevant features from a feature pool. The distinguishing features between pulsars and non-pulsars can significantly improve the performance of the classifier even if the data are highly imbalanced. In this work, an algorithm for feature selection called the K-fold Relief-Greedy (KFRG) algorithm is designed. KFRG is a two-stage algorithm. In the first stage, it filters out some irrelevant features according to their K-fold Relief scores, while in the second stage, it removes the redundant features and selects the most relevant features by a forward greedy search strategy. Experiments on the data set of the High Time Resolution Universe survey verified that ML models based on KFRG are capable of PCS, correctly separating pulsars from non-pulsars even if the candidates are highly class-imbalanced.

Multi-Channel Sensing System Utilizing Mott Memristors for Single-Wire Data Fusion and Back-End Greedy Strategy Data Recovery

This paper presents a novel Mott memristor-based multi-channel sensing system designed for the simultaneous processing of multiple sensing channels, employing single-wire data fusion and a greedy search strategy for back-end data recovery. Multiple channels of external stimulus information are simultaneously encoded into analog signals with varying frequencies, utilizing a Mott memristor array. Auxiliary circuits then convert the analog sensing signals into square wave signals which are further transformed into narrow (100 ns) pulse signals through pulse generation circuitry. Subsequently, these narrow pulse signals are fused into a single-wire signal by using an OR gate. At the back-end of the system, a greedy searching strategy is applied to accurately identify all frequencies within the fused pulse signal, enabling seamless analog-to-frequency conversion across multiple channels. The system is suitable for a wide range of sensors and can be directly connected to FPGAs for data processing, eliminating the need for traditional analogue front-end and ADC circuits and greatly reducing circuit complexity and power consumption. By leveraging the innovative capabilities of Mott memristors, the proposed system achieves precise analog-to-frequency conversion with significantly reduced power consumption.

Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method for Vehicular Safety Messages

Efficient routing in urban vehicular networks is essential for timely and reliable safety message transmission, and the selection of paths and relays greatly affects the quality of routing. However, existing routing methods usually face difficulty in finding the globally optimal transmission path due to their greedy search strategies or the lack of effective ways to accurately evaluate relay performance in intricate traffic scenarios. Therefore, we present a vehicular safety message routing method based on heuristic path search and multi-attribute decision-making (HMDR). Initially, HMDR utilizes a heuristic path search, focusing on road section connectivity, to pinpoint the most favorable routing path. Subsequently, it employs a multi-attribute decision-making (MADM) technique to evaluate candidate relay performance. The subjective and objective weights of the candidate relays are determined using ordinal relationship analysis and the Criteria Importance Through Intercriteria Correlation (CRITIC) weighting methods, respectively. Finally, the comprehensive utility values of the candidate relays are calculated in combination with the link time and the optimal relay is selected. In summary, the proposed HMDR method is capable of selecting the globally optimal transmission path, and it comprehensively considers multiple metrics and their relationships when evaluating relays, which is conducive to finding the optimal relay. The experimental results show that even if the path length is long, the proposed HMDR method gives preference to the path with better connectivity, resulting in a shorter total transmission delay for safety messages; in addition, HMDR demonstrates faster propagation speed than the other evaluated methods while ensuring better one-hop distance and one-hop delay. Therefore, it helps to improve the performance of vehicular safety message transmission in intricate traffic scenarios, thus providing timely data support for secure driving.

Discrete Robust Principal Component Analysis via Binary Weights Self-Learning.

Principal component analysis (PCA) is a typical unsupervised dimensionality reduction algorithm, and one of its important weaknesses is that the squared l2 -norm cannot overcome the influence of outliers. Existing robust PCA methods based on paradigm have the following two drawbacks. First, the objective function of PCA based on the l1 -norm has no rotational invariance and limited robustness to outliers, and its solution mostly uses a greedy search strategy, which is expensive. Second, the robust PCA based on the l2,1 -norm and the l2,p -norm is essential to learn probability weights for data, which only weakens the influence of outliers on the learning projection matrix and cannot be completely eliminated. Moreover, the ability to detect anomalies is also very poor. To solve these problems, we propose a novel discrete robust principal component analysis (DRPCA). Through self-learning binary weights, the influence of outliers on the projection matrix and data center estimation can be completely eliminated, and anomaly detection can be directly performed. In addition, an alternating iterative optimization algorithm is designed to solve the proposed problem and realize the automatic update of binary weights. Finally, our proposed model is successfully applied to anomaly detection applications, and experimental results demonstrate that the superiority of our proposed method compared with the state-of-the-art methods.

Day-ahead multi-modal demand side management in microgrid via two-stage improved ring-topology particle swarm optimization

The role of an energy management system is crucial today. However, the heavy reliance on fossil fuels as well as the growing gap between electricity demands and energy power generation have resulted in various global challenges, including energy shortages, high utility bills and greenhouse gas emissions. To address this issue, this paper presents a practical and comprehensive microgrid model that combines day-ahead multi-modal demand side management (DSM) and energy storage (ES) operation. The model aims to provide multiple optimal or near-optimal DSM suggestions to users, increasing their willingness to respond the suggestions and fully leveraging the benefits of DSM. Besides, the ES operation works in conjunction with DSM as an energy buffer for generated power, ensuring that user demands are always met. Particle swarm optimization(PSO) is improved to optimize the DSM model due to its merits, including simplicity, population-based structure, and effective learning mechanism. To balance the two key capabilities of PSO well, the exploration ability of PSO is enhanced by index-based ring topology, ensuring that the entire particle swarm can evenly diverge across the search space, while the exploitation ability of each sub-swarm is improved using a greedy search strategy, empowering each sub-swarm can effectively exploits their respective surroundings. To demonstrate the effectiveness of the proposed model, four different DSM strategies are designed in MG system for different purposes of cost saving, carbon emissions reduction, cost saving with load fluctuations stabilization, and emissions reduction with load fluctuation stabilization, respectively. Numerical experiments reveal advantages of the proposed ir3PSO that can search for qualified solutions with better diversity and higher accuracy in a single run. In addition, detailed sensitivities of key parameters, including load participation level and user acceptance, are also analyzed for reference by decision-makers.

Transferable adversarial distribution learning: Query-efficient adversarial attack against large language models

It is a challenging task to fool a text classifier based on deep neural networks under the black-box setting where the target model can only be queried. Among the existing black-box attacks, decision-based methods have a large query cost due to exponential perturbation space and greedy search strategy. Transfer-based methods, on the other hand, tend to overfit the surrogate model and thus fail when applied to unknown target models. In this paper, we propose a straightforward yet highly effective adversarial attack framework for black-box transformer-based models, thereby exposing vulnerabilities within large language models. Specifically, we leverage a fine-tuned large language model as a white-box surrogate model and optimize a distribution of adversarial text. This distribution is parameterized by a continuous-valued matrix based upon the surrogate model. To avoid overfitting of the distribution and improve its adversarial transferability, we incorporate an additional causal language model into our framework as a constraint model. Based on this constraint model, we add language model perplexity and semantic consistency as regularization terms during the distribution training process. To further reduce the number of queries to the target model, i.e., improve the threat level of examples drawn from our distribution, we employ a geometric loss strategy to ensure that the distribution training process learns the optimal perturbation. Extensive experimental studies have been carried out on benchmark datasets and the results demonstrate significant improvement on the performance and query efficiency under black-box setting in comparison with well-established approaches. Our approach achieves an 80.98% reduction in BERT model accuracy while consuming only 21.86% of the query times required by prior attacks.

Bi‐RRT*: An Improved Bidirectional RRT* Path Planner for Robot in Two‐Dimensional Space

Path planning is one of the fundamental issues in the research field of robot. In the last decades, Rapidly‐exploring Random Tree star (RRT*) was one of the chosen path planner for robot with its probabilistically complete. In this paper, an improved bidirectional RRT* path planner for robot is proposed (Bi‐RRT*), which has planned path with safety and smoothly guarantee. The main contributions of this paper include, firstly, combining the backtracking idea of the Quick‐RRT algorithm with the greedy search strategy of the RRT‐Connect algorithm, which can reduce the path cost and improve the operation efficiency of the algorithm. Second, in order to further reduce the running time of the algorithm, a new collision detection algorithm based on cross‐product is proposed to replace the conventional collision detection strategy. To ensure the quality of the path, two post‐processing strategies, path optimization strategy and path smoothing strategy, are proposed. Path optimization strategy is based on triangle inequality, and two optimization methods are proposed to effectively reduce the path cost. The path smoothing strategy based on the Bezier curve improves the continuity of a smooth arc and is better applied to path smoothing. Simulation results for both virtual and real environments show the advantages of combining Quick‐RRT and RRT‐Connect and verify the effectiveness of the proposed collision detection algorithm. Compared with similar algorithms, the proposed Bi‐RRT algorithm has higher operational efficiency, smaller path cost, smoother planned paths, and improved path quality. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

A task scheduling algorithm with deadline constraints for distributed clouds in smart cities.

Computing technologies and 5G are helpful for the development of smart cities. Cloud computing has become an essential smart city technology. With artificial intelligence technologies, it can be used to integrate data from various devices, such as sensors and cameras, over the network in a smart city for management of the infrastructure and processing of Internet of Things (IoT) data. Cloud computing platforms provide services to users. Task scheduling in the cloud environment is an important technology to shorten computing time and reduce user cost, and thus has many important applications. Recently, a hierarchical distributed cloud service network model for the smart city has been proposed where distributed (micro) clouds, and core clouds are considered to achieve a better network architecture. Task scheduling in the model has attracted many researchers. In this article, we study a task scheduling problem with deadline constraints in the distributed cloud model and aim to reduce the communication network's data load and provide low-latency services from the cloud server in the local area, hence promoting the efficiency of cloud computing services for local users. To solve the task scheduling problem efficiently, we present an efficient local search algorithm to solve the problem. In the algorithm, a greedy search strategy is proposed to improve the current solutions iteratively. Moreover, randomized methods are used in selecting tasks and virtual machines for reassigning tasks. We carried out extensive computational experiments to evaluate the performance of our algorithm and compared experimental results with Swarm-based approaches, such as GA and PSO. The comparative results show that the proposed local search algorithm performs better than the comparative algorithms on the task scheduling problem.

Query-Efficient Black-Box Adversarial Attack With Customized Iteration and Sampling.

It is a challenging task to fool an image classifier based on deep neural networks under the black-box setting where the target model can only be queried. Among existing black-box attacks, transfer-based methods tend to overfit the substitute model on parameter settings. Decision-based methods have low query efficiency due to fixed sampling and greedy search strategy. To alleviate the above problems, we present a new framework for query-efficient black-box adversarial attack by bridging transfer-based and decision-based attacks. We reveal the relationship between current noise and variance of sampling, the monotonicity of noise compression, and the influence of transition function on the decision-based attack. Guided by the new framework, we propose a black-box adversarial attack named Customized Iteration and Sampling Attack (CISA). CISA estimates the distance from nearby decision boundary to set the stepsize, and uses a dual-direction iterative trajectory to find the intermediate adversarial example. Based on the intermediate adversarial example, CISA conducts customized sampling according to the noise sensitivity of each pixel to further compress noise, and relaxes the state transition function to achieve higher query efficiency. Extensive experiments demonstrate CISA's advantage in query efficiency of black-box adversarial attacks.

Multi-satellite Mission Planning Algorithm Based on Preemptive Priority Model

<p>Aiming at the problems of high time-consuming and insufficient guarantee of high-priority task completion in the large-scale multi-satellite multi-observation mission planning application, a multi-satellite multi-mission planning model based on preemptive priority model is proposed. Combined with the whole-neighborhood greedy search strategy, an improved tabu search algorithm is designed, taking the combination of satellite observation windows as the decision variables. The designed algorithm realizes an efficient solver to the proposed mission planning model. The simulation results show that the average solving time of the algorithm is 144s under the problem scale of 100 satellites and 3000 tasks. Compared with the existing algorithm, the calculation time is shortened by at least 75% under the premise that the total number of planning tasks is comparable, which is more in line with the high time efficiency requirements for satellite mission planning in engineering applications.</p> <p> </p>

Asymmetric Action Abstractions for Planning in Real-Time Strategy Games

Action abstractions restrict the number of legal actions available for real-time planning in zero-sum extensive-form games, thus allowing algorithms to focus their search on a set of promising actions. Even though unabstracted game trees can lead to optimal policies, due to real-time constraints and the tree size, they are not a practical choice. In this context, we introduce an action abstraction scheme which we call asymmetric action abstraction. Asymmetric abstractions allow search algorithms to “pay more attention” to some aspects of the game by unevenly dividing the algorithm’s search effort amongst different aspects of the game. We also introduce four algorithms that search in asymmetrically abstracted game trees to evaluate the effectiveness of our abstraction schemes. Two of our algorithms are adaptations of algorithms developed for searching in action-abstracted spaces, Portfolio Greedy Search and Stratified Strategy Selection, and the other two are adaptations of an algorithm developed for searching in unabstracted spaces, NaïveMCTS. An extensive set of experiments in a real-time strategy game shows that search algorithms using asymmetric abstractions are able to outperform all other search algorithms tested.

An improved optimization algorithm for a multi-depot vehicle routing problem considering carbon emissions

In a multi-depot vehicle routing problem (MDVRP) of same-city delivery, driving distance and actual loading can greatly influence the amount of carbon emissions generated. This paper considers fuel and carbon emission costs as part of total costs, proposes a MDVRP with minimized logistics costs and driven distance, and then establishes a mixed integer programming model. An improved chemical reaction optimization algorithm is also designed by considering this problem's characteristics (i.e., a greedy search strategy is presented to generate an initial population), and two coding approaches (i.e., two-part coding and matrix coding) are applied prior to designing four chemical reaction operators. The simulation experiment is carried out using a set of a random instances and the experimental results demonstrate that one can reduce carbon emissions by driving extra lesser distances, providing a methodological guide for MDVRPs with logistics costs and carbon emissions.

A Novel Feature Selection Method With Neighborhood Rough Set and Improved Particle Swarm Optimization

Neighborhood rough set is an excellent mathematical tool to carry out feature selection on both numerical and categorical dataset. However, conventional feature selection algorithms usually use greedy heuristic search strategies, which is easy to trap in the local extreme point. In this study, we propose a hybrid feature selection model that combines neighborhood rough set with an improved particle swarm optimization. The model computes the dependency degree of decision from neighborhood rough set as the objective function to evaluate the selected features, and then takes advantages of PSO’s stochastic search to discover the optimal solution more effectively. In order to improve the global search ability and alleviate the stagnation in local optima, the model modifies PSO part by adopting genetic operators and Levy flight. To evaluate the performance of this model, we implement experiments using twelve benchmark datasets and two classifiers ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN and SVM). Compared with five representative filter-based approaches, experimental results show that our model can not only guarantee the stronger classification ability but also remove more redundant features in most datasets.

CRank: Reusable Word Importance Ranking for Text Adversarial Attack

Deep learning models have been widely used in natural language processing tasks, yet researchers have recently proposed several methods to fool the state-of-the-art neural network models. Among these methods, word importance ranking is an essential part that generates text adversarial examples, but suffers from low efficiency for practical attacks. To address this issue, we aim to improve the efficiency of word importance ranking, making steps towards realistic text adversarial attacks. In this paper, we propose CRank, a black box method utilized by our innovated masking and ranking strategy. CRank improves efficiency by 75% at the ’cost’ of only a 1% drop of the success rate when compared to the classic method. Moreover, we explore a new greedy search strategy and Unicode perturbation methods.

Efficient Convolutional Neural Architecture Search for Remote Sensing Image Scene Classification

As a fundamental but challenging task in the interpretation of remote sensing images, scene classification plays an important role in various applications and has become an active research topic. Many previous works have demonstrated the remarkable performance of the deep convolutional neural networks (CNNs) for remote sensing scene classification. However, the progress made by CNN-based methods for scene classification has gradually reached saturation in recent years, due to the serious dependence on the pretrained CNN models, the limitations of manually designed network architecture and the disadvantages of existing data sets. In this article, a new paradigm to automatically design a suitable CNN architecture for scene classification is investigated. We propose an efficient architecture search framework to discover optimal network architectures in continuous search space with the gradient-based optimization method. Our framework consists of two stages: the search phase and the evaluation phase. During the search process, a greedy and progressive search strategy is introduced to search network building blocks (i.e., cells) through bilevel optimization. Besides, we propose a simple architecture regularization scheme to further improve the search efficiency and the robustness of discovered architectures. After the search process, the optimal cell architectures are determined and then repeatedly stacked to construct the final network for evaluation. For the data set, we propose a mergence strategy to build a new large-scale remote sensing scene image data set that contains rich scene categories and image diversity, making it feasible to find a new CNN model with strong generalization ability for scene classification. Extensive experiments demonstrate the efficiency of the proposed search strategies and the impressive classification performance of searched CNN architectures on seven public benchmark data sets, including four large-scale data sets and three small-scale data sets.