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IEEE International Conference on Progress in Informatics and Computing (IEEE PIC)

作者： Li, Haotian Zhang, Baohang Li, Jiayi Zheng, Tao Yang, Haichuan Univ Toyama Fac Engn Toyama 9308555 Japan

ISBN: (纸本)9781665426558

The water wave optimization (WWO) algorithm is a new cluster intelligence search method. It has the advantages of a small population size and simple parameter configuration. It is used to build an efficient mechanism for searching in high-dimensional solution spaces. However, it has a proclivity for becoming stuck in local optima. Coincidentally, the sparrow search algorithm (SSA) has good exploration ability. By combining WWO and SSA, we propose a hybrid algorithm, called WWOSSA. The experimental results of the WWOSSA algorithm based on 29 benchmark functions of IEEE CEC2017 have good optimization ability and a fast convergence rate.

关键词： water wave optimization sparrow search algorithm hybrid algorithm

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New approach of computing task offloading for IOV based on sparrow search optimization strategy

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2025年 46卷

作者： Zhang, Degan Wang, Xiaoyang Zhang, Jie Zhang, Ting Chen, Lu Chen, Hongtao Honglin, E. Tianjin Univ Technol Tianjin Peoples R China Beijing Jiaotong Univ Beijing Peoples R China Tianjin Univ Sport Tianjin Peoples R China

With the rapid development of the Internet of Vehicles (IoV), the computation and communication demands of vehicles are increasing. The traditional centralized computing model can no longer meet these demands. Consequently, task offloading techniques have become crucial for enhancing computational performance and reducing vehicle load in IoV. In this paper, we propose a new method of task offloading for IoV computing based on the sparrow search optimization strategy. Specifically, we address the multifactorial influences on task offloading. Firstly, we design an offloading model that integrates multiple optimization objectives, such as delay and energy consumption. Secondly, we develop a fitness function that balances delay and energy consumption to evaluate and select task offloading strategies. Additionally, we design a network access model to maintain network access stability. Finally, we conduct an iterative optimization search for the offloading strategy using an improved sparrow search optimization algorithm. Through extensive simulation experiments and real-world scenario tests, we validated the effectiveness and performance advantages of the proposed method. The experimental results demonstrate that our new IoV task offloading method, based on the improved sparrow search optimization algorithm, enhances computational performance while reducing vehicle load, showing great potential for applications in the field of IoV task offloading.

关键词： Computation offloading Edge computing sparrow search algorithm IoV

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A Novel sparrow search Scheme Based on Enhanced Differential Evolution Operator

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 1253-1268页

作者： Liu, Zewei Hu, Chunqiang Xiang, Tao Hu, Pengfei Li, Xingwang Yu, Jiguo Chongqing Univ Sch Big Data & Software Engn Chongqing 400030 Peoples R China China Southern Power Grid Joint Lab Cyberspace Secur Guangzhou 510530 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400030 Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan 250100 Peoples R China Henan Polytech Univ Coll Phys & Elect Informat Engn Jiaozuo 454099 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Qilu Univ Technol Big Data Inst Jinan Peoples R China

The sparrow search algorithm (SSA) represents a novel approach within swarm intelligence optimization, introduced in recent years. Renowned for its minimal control parameters and straightforward implementation, the SSA algorithm has swiftly emerged as one of the most proficient and attractive optimization methodologies. Despite its significant accuracy in solutions and rapid convergence, the SSA is hampered by its underperformance in tackling intricate optimization challenges and an imbalanced distribution between exploration and exploitation capabilities. Consequently, this paper proposes a dynamic two-factor sparrow search algorithm based on an enhanced differential evolution operator (DSSADE). Initially, to harmonize both the global and local search capabilities and enhance the convergence speed of the SSA algorithm, a dynamic two-factor mode is put forward. Subsequently, a tolerance mechanism is devised to gauge the algorithm's likelihood of gravitating towards local optima. Furthermore, an embedded enhanced differential evolution operator (EDE) fortifies the SSA's capacity to escape local optima. Notably, experimental results obtained from both classical and CEC2017 benchmark functions exhibit substantial confirmation and endorse the superior efficacy of DSSADE compared to various SSA variants and other cutting-edge swarm intelligent algorithms.

关键词： Heuristic algorithms Statistics Sociology Vectors Convergence Accuracy Metaheuristics differential evolution operator disturbance sparrow search algorithm tolerance mechanism

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Efficient Path Planning and Tracking Control of Autonomous Vehicles in Park Scenarios Based on sparrow search and Stepwise Prediction

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2024年第4期10卷 8346-8361页

作者： Zhang, Haotian Yu, Yali Liu, Yonggang Jiang, Fengyang Chen, Zheng Zhang, Yuanjian Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Sch Mech & Vehicle Engn Chongqing 400044 Peoples R China China Highway Vehicle & Machinery Co Ltd Beijing 100013 Peoples R China Kunming Univ Sci & Technol Fac Transportat Engn Kunming 650500 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU England

Electrification and intelligence dominate the developing trend of automobiles. Except for the tremendous progress in electrification, autonomous driving is widely investigated with the advancement of perception and communication technologies, while proper path planning and rigorous tracking control in autonomous driving remain challenging, especially in park scenarios. To cope with it, an efficient long short-term path planning algorithm is first proposed to compensate the lack of global driving information in park scenarios, and a fifth-order polynomial and an improved artificial potential field method are incorporated to devise the candidate trajectory clusters for obstacle avoidance. Second, the collision detection of candidate trajectories is attained with the support of a rectangular vehicle model, and the dangerous trajectories of potential collisions in the candidate clusters are screened out. Then, three evaluation indicators considering safety, comfort, and lane change efficiency are weighed to establish the multiperformance objective function, and the sparrow search algorithm is exploited to determine the optimal obstacle avoidance trajectory. Finally, the stepwise prediction with multiple prediction models is exploited to achieve a precise trajectory. The simulation and experiment results manifest that the proposed method enables to achieve precise path planning and tracking and ensures satisfactory safety, comfort, and real-time performance in park scenes.

关键词： Path planning Autonomous vehicles Collision avoidance Trajectory Planning Real-time systems Predictive models Improved artificial potential field model park scenes path planning sparrow search algorithm tracking control

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SS-WDRN: sparrow search optimization-based weighted dual recurrent network for software fault prediction

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第2期66卷 1037-1064页

作者： Brundha Elci, J. Nandagopalan, S. Visvesvaraya Technol Univ Vemana Inst Technol Belagavi 590018 India

Predicting software faults at the primary stage is a challenging role for software engineers and tech industries. During the development of software projects, it is necessary to predict the number of probable faults to have occurred on software rather than detecting whether the software modules are faulty or not. Discovering the number of expected faults helps software professionals to develop more reliable and high-quality software systems. However, the prediction model's performance gets affected while dealing with complicated software projects with increased cost factors such as time, effort, and resources. Therefore, to address the issue associated with handling complex software projects, a novel weighted dual cross-recurrent network-based levy sparrow search (WDCRN-LSS) model is proposed in this paper. The WDCRN-LSS approach by learning the data features with optimal hyperparameters accurately predicts the expected software faults in an earlier phase. Here, 17 PROMISE datasets containing 20 features each are utilized as input data for the proposed WDCRN-LSS model. The data inconsistencies are eliminated and then transformed to a suitable format for training through normalization, data transformation, and label encoding procedures. The preprocessed data are then trained using the proposed WDCRN-LSS model for the prediction of the expected number of software faults in the projects. With the excellent learning capability of feature representations, the proposed WDCRN-LSS model predicts software faults on upcoming/under-development software projects precisely. Thus, the proposed WDCRN-LSS model enhances software quality and minimizes cost factors such as time, resources, and effort that are depleted in developing software. The proposed WDCRN-LSS model's efficiency is investigated by utilizing evaluation measures namely error rate, precision, recall, F1-measure, the area under the curve, accuracy, and specificity. The experimental result manifests the efficiency of

关键词： Software fault prediction Dual cross-recurrent network Weighted average ensemble sparrow search algorithm Levy flight Software projects

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Maximum Power Tracking for Centralized Temperature Difference Power Generation System Based on Elman Neural Network Combined With Improved sparrow search

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IEEE ACCESS 2023年 11卷 109169-109178页

作者： He, Xinying Chen, Yan Du, Qian Feng, Lulu Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan 030024 Shanxi Peoples R China

Since the thermoelectric generation (TEG) sheets will be placed in places with different temperature gradients, it leads to multiple peaks in the duty-power (D-P) characteristic curve of a centralized TEG system under non-uniform temperature distribution (NTD). For this reason, this paper proposes an ENN-ISSA control algorithm, which combines the Elman neural network (ENN) with the sparrow search algorithm (SSA) by adding firefly perturbation. The ENN obtains the centralized TEG system's single-input and single-output fitting curves, after which the firefly perturbation is introduced into the SSA algorithm. Then the improved SSA algorithm is used to realize the maximum power point tracking (MPPT) control based on the fitted curves. Based on building a centralized TEG system Simulink model and analyzing the output characteristics of the TEG module, temperature constancy experiments, temperature change experiments, and accuracy analysis were conducted. The results of these simulation experiments all show that the algorithm can track the global maximum power point (GMPP) quickly and accurately in the duty-power (D-P) curve with multiple peaks compared with the perturbation observation method and particle swarm algorithm.

关键词： Centralized thermoelectric generation system sparrow search algorithm thermoelectric generator Elman neural network centralized maximum power point tracking

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A Deployment Optimization algorithm for WSNs based on Adaptive Virtual Force Disturbance sparrow search 21

A Deployment Optimization Algorithm for WSNs based on Adapti...

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Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

作者： Xiao-Qiang Zhao Min Liu Yan-Peng Cui Yin-Di Yao School of Artificial Intelligence Xi'an University of Posts and Telecommunications Xi'an University of Posts and Telecommunications China School of Artificial Intelligence Xi'an University of Posts and Telecommunications Xi'an University of Posts and Telecommunications China Key Laboratory of Universal Wireless Communications Beijing University of Posts and Telecommunications Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

When environmental monitoring and target tracking are carried out in harsh and unattended environment, the deployment of nodes in wireless sensor networks affects the monitoring effect and deployment cost of the network. In order to effectively improve network coverage and utilization of network resource, and achieve the best coverage effect by deploying the least number of network nodes, a coverage optimization strategy for wireless sensor networks based on adaptive virtual force disturbance sparrow search algorithm is proposed. Firstly, the adaptive virtual force algorithm is designed for different scale deployment, and the sparrow search algorithm is disturbed by the adaptive virtual force algorithm, which accelerates the convergence speed of the algorithm. Secondly, by analyzing the scrounger characteristics of the sparrow search algorithm, an elite selection strategy is designed to improve the sparrow search algorithm, which overcomes the disadvantage that the algorithm is easy to fall into local optimum in the later iteration stage and improves the global convergence performance of the algorithm. The simulation results indicate that compared with the coverage efficient virtual force algorithm, the sparrow search algorithm and the virtual force-directed particle swarm optimization, this final coverage rate of this algorithm increased by 8.6%, 4.6%, 4.1%, and coverage efficiency improved by 6.3%, 3.3%, 2.9%, respectively. In addition, the algorithm has obvious performance advantages in terms of moving distance of nodes.

关键词： Adaptive virtual force Coverage performance Deployment optimization sparrow search algorithm

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Optimized Support Vector Machine using sparrow search Optimization for Osteoporosis Disease Diagnosis 2

Optimized Support Vector Machine using Sparrow Search Optimi...

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2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Kunjali, Sharifa Malarvizhi, S. Govt Arts & Sci Coll Women Dept Comp Sci Barugur Tamil Nadu India Thiruvalluvar Govt Arts Coll Dept Comp Sci Rasipuram Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

Osteoporosis might potentially reduce fracture risk and enhance quality of life if detected early. A prominent intelligence technique for diagnosing osteoporosis is support vector machines (SVMs), and choosing the right SVM parameter is essential for precise model classification. This research study optimizes the kernel function parameter and penalty factor of SVM using the new and extremely efficient sparrow search algorithm (SSA) based on partial opposition-based learning (POBL) called POSSA called POSSA-SVM osteoporosis disease detection model. In comparison to the variants and conventional models, experiments demonstrate that the POSSA-SVM diagnostic model efficiently increases the accuracy of osteoporosis disease diagnosis. It also claims a strong optimization ability and a rapid convergence time.

关键词： support vector machine sparrow search algorithm partial opposition-based learning hyperparameters optimization salp swarm algorithm Osteoporosis detection

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A combined model based on sparrow search optimized BP neural network and Markov chain for precipitation prediction in Zhengzhou City, China

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AQUA-WATER INFRASTRUCTURE ECOSYSTEMS AND SOCIETY 2022年第6期71卷 782-800页

作者： Guo, Nan Wang, Zhaocai Nanjing Inst Technol Sch Math & Phys Nanjing 211167 Peoples R China Shanghai Ocean Univ Coll Informat Shanghai 201306 Peoples R China

Simulation and prediction of precipitation time series changes are important for revealing global climate change patterns and understanding surface hydrological processes. However, precipitation is influenced by a variety of factors together, showing the characteristics of nonlinear variation patterns. Given that backpropagation (BP) neural network has a strong mapping ability for nonlinear fitting, we consider using BP neural network for precipitation prediction, then use sparrow search algorithm (SSA) to optimize BP network initial threshold and weight information to improve the efficiency of precipitation prediction. To further enhance model predictive performance, the Markov model is employed to predict the residual series of the SSA-BP model, so as to finally construct a combined SSA-BP-Markov model of precipitation. In this paper, the model is used to simulate the rainfall prediction in Zhengzhou City, Henan Province, China, and to compare and analyze with the other traditional models. The empirical prediction results show that the SSA-BP-Markov model is more accurate and the convergence of the algorithm is better. The model provides a new way of thinking for precipitation prediction and is also useful for predicting precipitation in other regions.

关键词： backpropagation neural network Markov model precipitation prediction sparrow search algorithm

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CF-PMSS: Collaborative Filtering Based on Preference Model and sparrow search 14th

CF-PMSS: Collaborative Filtering Based on Preference Model a...

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14th International Conference on Swarm Intelligence (ICSI)

作者： Song, Wei Li, Shuya North China Univ Technol Sch Informat Sci & Technol Beijing 100144 Peoples R China

ISBN: (纸本)9783031366246;9783031366253

Collaborative filtering (CF) is a widely used recommendation method that can be evaluated according to the rating prediction or top-N recommendations. In rating prediction-based CF, it is important to refine the bias of the original ratings and select users with a greater degree of similarity. To achieve this rating refinement, we transform the countable raw ratings into real numbers using a preference model. To identify similar users, we add item types to the similarity calculation, and use the sparrow search algorithm to enhance the effect of user clustering. The performance of the proposed CF based on a preference model and sparrow search is compared with that of related heuristic-based CFs, and the results demonstrate the superiority of the proposed approach in terms of accuracy and correlation.

关键词： Recommender system Collaborative filtering Preference model sparrow search algorithm Comprehensive similarity

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