检索结果-内蒙古大学图书馆

作者： Yue-jin ZHOU School of Management and Engineering Nanjing University

The methods of solving resource allocation are mainly heuristic algorithms which could not solve resource allocation problems in cloud computing. The hybrid optimization algorithm is studied to solve the problem. There are many different hybrid optimization algorithms. Our research hopes to find a simple and effective method. We select a combination optimization algorithm of the genetic and ant colony algorithms. In the early phase of this algorithm, with the help of the wide range search capabilities of the genetic algorithm, it finds a better solution;in the later stage of this algorithm, with the help of positive feedback and efficiency of the ant colony algorithm, it finds the optimal solution. In addition, the two algorithms convergence conditions and the way of how to make the better solution of the genetic algorithm translate into the initial pheromone distribution provisions of the ant colony algorithm are set up. At last, the algorithm was realized with a simulation environment, and a specific example was made by comparative analysis to verify the correctness and effectiveness of the algorithm

关键词： Cloud computing Resource allocation hybrid optimization algorithm Allocation model

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Traffic travel service selection based on hybrid optimization algorithm 21

Traffic travel service selection based on hybrid optimizatio...

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Proceedings of the 3rd World Symposium on Software Engineering

作者： Wanchun Yang Chenxi Zhang School of Sciences Shandong Jiaotong University China School of Software Engineering Tongji University China

ISBN: (纸本)9781450384094

With the development of information technology in transportation industry, it becomes an important application of intelligent transportation system to provide real-time and effective travel services. The existing traffic travel service composition model is based on the assumption of single request, and it can not build composition according to granularity. Aiming at the problem of requests and granularity, from the two dimensions of transaction and quality of service (QoS), this paper presents an evaluation model of service composition which considers concurrent requests and multi-granularity. Based on the model, the paper proposed a method based on hybrid optimization algorithm. The proposed algorithm combined particle swarm optimization, crossover and mutation operators with priority, simulated annealing algorithm to get the optimal value. Experimental results show that our approach can guarantee QoS of service selection with low time cost.

关键词： Multi-granularity Transactional property Service selection hybrid optimization algorithm Quality of service(QoS) Concurrent requests

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A Novel hybrid optimization algorithm Combined with BBO and ACO

A Novel Hybrid Optimization Algorithm Combined with BBO and ...

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第三十九届中国控制会议

作者： Zhuo Dai Qian Ma Dongdong Zhao Shi Yan School of Information Science and Engineering Lanzhou University

This paper proposes a novel hybrid optimization algorithm based biogeography-based optimization(BBO) and ant colony optimization(ACO).Specifically,BBO is utilized for preliminary search and then the final search is conducted by using ACO on the preliminary results,which makes the hybrid algorithm have powerful search ability in the solution ***,the algorithm is tested on 6 well-known benchmarks for comparing with the 4 others existing optimization algorithms,including2 algorithms were proposed by the coauthor ***,the simulation results show that the proposed hybrid BBO-ACO has better performance than the existing others.

关键词： hybrid optimization algorithm Biogeography-Based optimization Ant Colony optimization

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Real-Time Dynamic Path Planning of Mobile Robots: A Novel hybrid Heuristic optimization algorithm

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SENSORS 2020年第1期20卷 188-188页

作者： Wu, Qing Chen, Zeyu Wang, Lei Lin, Hao Jiang, Zijing Li, Shuai Chen, Dechao Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China Hong Kong Polytech Univ Dept Comp Hung Hom Kowloon Hong Kong 999077 Peoples R China

Mobile robots are becoming more and more widely used in industry and life, so the navigation of robots in dynamic environments has become an urgent problem to be solved. Dynamic path planning has, therefore, received more attention. This paper proposes a real-time dynamic path planning method for mobile robots that can avoid both static and dynamic obstacles. The proposed intelligent optimization method can not only get a better path but also has outstanding advantages in planning time. The algorithm used in the proposed method is a hybrid algorithm based on the beetle antennae search (BAS) algorithm and the artificial potential field (APF) algorithm, termed the BAS-APF method. By establishing a potential field, the convergence speed is accelerated, and the defect that the APF is easily trapped in the local minimum value is also avoided. At the same time, by setting a security scope to make the path closer to the available path in the real environment, the effectiveness and superiority of the proposed method are verified through simulative results.

关键词： hybrid optimization algorithm mobile robot real-time path planning dynamic obstacle avoidance beetle antennae search algorithm (BAS)

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A new hybrid global optimization algorithm 14

A new hybrid global optimization algorithm

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14th International Conference on Computational Intelligence and Security (CIS)

作者： Ding, Wang Xian Univ Sci & Technol Coll Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9781728101699

Focusing on the disadvantages of gravitation search algorithm (GSA) and artificial bee colony (ABC), such as low convergence precision, slow convergence, and this paper proposed a new hybrid optimization algorithm NHA based on GSA and ABC. NHA balance exploitation and exploration. Numerical experiments show that NHA has good results in high dimensions.

关键词： gravitation search algorithm artificial bee colony hybrid optimization algorithm

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A hybrid optimization algorithm for Pairwise Test Suite Generation

A Hybrid Optimization Algorithm for Pairwise Test Suite Gene...

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IEEE International Conference on Information and Automation

作者： Rongzhi Qi Zhijian Wang Ping Ping Shuiyan Li College of Computer and Information Hohai University College of Science Hohai University

ISBN: (纸本)9781467391054

Pairwise testing is an effective combinatorial test generation technique that can generate relative small test suite to cover all pairs of parameter values at least once. Genetic algorithm has been used for pairwise test suite generation by some researchers. In order to improve the performance of genetic algorithm, this paper proposes a hybrid optimization algorithm by augmenting genetic algorithm with two-stage hill climbing. The first stage is to improve all the individuals after genetic operations. The second stage is to improve the best solution of the current generation at the end of each generation. A series of experiments are conducted to evaluate the proposed algorithm. Experiment results show that the proposed algorithm is very competitive with respect to other approaches reported in the literature.

关键词： Pairwise testing hybrid optimization algorithm Genetic algorithm Hill climbing

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optimization of staggered peak intermittent pumping operation scheduling of pumping unit well clusters under wind, solar and energy storage microgrid with improved adaptive GAPSO hybrid algorithm

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2025年 252卷

作者： Wang, Jun Tan, Chaodong Chen, Peiyao Lu, Mei Feng, Gang Gao, Xiaoyong Liu, Bin Jing, Linru China Univ Petr Coll Artificial Intelligence Beijing 102249 Peoples R China CNPC Changging Oilfield Co Res Inst Oil & Gas Technol Xian 710000 Shanxi Peoples R China Xian Supcon World Technol Dev Co Ltd Xian 710016 Shanxi Peoples R China

Using manual experience-developed intermittent pumping systems for clustered wells in low-permeability reservoirs in the middle and late stages of production often leads to problems such as high operating costs. After the wind and photovoltaic power generation is introduced into the intermittent pumping system of pumping well clusters, the system faces deficiencies such as low consumption capacity of green power. For this reason, with the objectives of the lowest daily operating cost (including the operation and maintenance cost of the source-side equipment, the power purchase cost and the power sale revenue generated from the interaction with the grid) and the highest green power consumption rate of the intermittent pumping system, an optimization model for the staggered peak intermittent pumping operation and scheduling of pumping well clusters under Wind, Solar and Energy Storage microgrids was established by taking into account the power constraints of the devices at the source side and the production constraints of the wells at the load side. The improved adaptive GAPSO hybrid algorithm is used to solve the model. Compared with the results of the traditional optimization algorithm, the daily operation cost of the scheduling scheme solved by the improved algorithm is reduced from RMB 170 to RMB 150, the green power consumption rate is increased from 83 % to 86 %, and the daily production of the well cluster is also increased by 4.86 %. The case study shows that the proposed model can effectively improve the system's green power consumption capacity by adjusting the charging and discharging power of the storage battery and the power purchased and sold from the grid, and minimizing the system's daily operating cost while ensuring that the pumping well cluster meets the production demand. All simulations are performed in MATLAB by a computer with a 13th Gen Intel(R) Core(TM) i5-13500H CPU at 2.60 GHz with 16 GB of RAM.

关键词： Wind and photovoltaic power generation Pumping unit well cluster Staggered peak intermittent pumping operation Optimal scheduling hybrid optimization algorithm Genetic algorithm and particle swamp optimization

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A novel lightweight combinatorial optimization strategy based on ASA-NLPQL optimization algorithm for front seat skeleton of a passenger car

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ADVANCES IN ENGINEERING SOFTWARE 2025年 205卷

作者： Zhou, Xuan Ju, Jun Long, Jiangqi Wenzhou Univ Coll Mech & Elect Engn Chashan St Wenzhou 325035 Peoples R China

This study presents a novel combinatorial optimization strategy to address the challenges of lightweight design for automotive seat skeletons. The proposed approach integrates surrogate modeling with a hybrid optimization algorithm to enhance computational efficiency and solution accuracy. The hybrid algorithm leverages the strengths of global and gradient-based optimization methods by combining Adaptive Simulated Annealing and Non-Linear Programming by Quadratic Lagrangian (ASA-NLPQL). The effectiveness of the ASA-NLPQL hybrid algorithm is validated through two numerical function examples and an engineering optimization case study, and it is subsequently applied to a real-world case study focused on the lightweight optimization design of an automotive seat skeleton. Firstly, the finite element model for modal analysis of the entire front seat skeleton of a passenger car is developed and validated through experimental tests. Key design variables are identified via sensitivity analysis to guide the optimization process. Subsequently, a surrogate model is constructed using sample points generated by optimal Latin hypercube sampling. The combined optimization strategy, based on the surrogate model and hybrid algorithm, is then applied to the optimal design of the passenger car seat skeleton. optimization results demonstrate a 22.3% reduction in seat weight with negligible impact on vibration performance. Finally, the optimized seat skeleton undergoes various strength tests, with the results confirming that it meets the required strength criteria. These findings demonstrate the effectiveness of the proposed optimization strategy and provide a reliable reference for similar engineering optimization challenges.

关键词： Automotive seat skeleton hybrid optimization algorithm Combinatorial optimization strategy Lightweight optimization design Surrogate model

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Dynamic optimization can effectively improve the accuracy of reference evapotranspiration in southern China

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 230卷

作者： Xiao, Xiang Xiao, Ziniu Liu, Xiaogang Gu, Xinqin Di, Yanfeng Wu, Lifeng Zhang, Zexi Lv, Mingli Kunming Univ Sci & Technol Fac Modern Agr Engn Kunming 650500 Yunnan Peoples R China Chinese Acad Sci Inst Atmospher Phys State Key Lab Numer Modeling Atmospher Sci & Geoph Beijing Peoples R China Univ Chinese Acad Sci Coll Earth & Planetary Sci Beijing Peoples R China Hohai Univ Coll Agr Sci & Engn Nanjing 210098 Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog State Key Lab Desert & Oasis Ecol Key Lab Ecol Safety & Sustainable Dev Arid Lands Urumqi Peoples R China Shanghai Huawei Controllable Agr Technol Grp Co LT Shanghai Peoples R China

Rapid advancements in numerical simulation have endowed machine learning with abundant input features. Dynamic optimization of hyperparameters and feature combinations effectively captures intricate meteorological interactions, which is crucial for precisely estimating reference evapotranspiration (ET0). Utilizing NOAH data from the Global Land Data Assimilation System (GLDAS), we assessed the simulation accuracy of daily meteorological variables and ET0 across 16 stations in southern China. We introduced a hybrid model (HM) amalgamating Grey Wolf optimization (GWO), Particle Swarm optimization (PSO), and Extreme Gradient Boosting (XGBoost). Our findings reveal that HM's GLDAS-NOAH outputs surpass those from Inverse Distance Weighting (IDW) and Convolutional Neural Networks (CNN), reducing average RMSEs for Tmax, Tmin, RH, Wind, and Rs by 32.6%, 47.4%, 43.3%, 38.7%, and 27.3%, respectively, and by 31.1%, 29.3%,-3.3%, 5.6%, and 19.0%, culminating in average ET0 RMSE reductions of 30.8% and 14.1%. Tmax exhibited the highest accuracy, followed by RH, Tmin, Rs, and Wind, with mean NRMSEs of 0.08 (0.04-0.24), 0.10 (0.05-0.14), 0.19 (0.03-3.00), 0.21 (0.14-0.29), and 0.35 (0.27-0.48). The mean NRMSE for ET0 was 0.21 (0.16-0.31). Excluding RH, HM's outputs reduced ET0 errors more effectively than IDW and CNN. RH contributed most to ET0 enhancement, followed by Rs, Tmax, Wind, and Tmin, with average NRMSE reductions of 13.24%, 10.07%, 3.28%, 2.75%, and 2.32%. HM's feature input combinations ranged from 6 to 10, most frequently 7. RH incorporated the most input features, averaging 8.6 per station. Equivalent variables emerged as the most significant features for Tmax, RH, Rs, and Wind, with average Gain values of 0.723, 0.456, 0.749, and 0.376, respectively. For Tmin, soil temperature was the predominant contributor, with an average Gain of 0.884. These results underscore that optimizing input feature combinations and hyperparameters in HM markedly enhances the simulation ac

关键词： hybrid optimization algorithm Feature importance analysis Dynamic optimization hybrid model Reference evapotranspiration Machine Learning

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Knowledge-assisted hybrid optimization strategy of large-scale crude oil scheduling integrated production planning

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COMPUTERS & CHEMICAL ENGINEERING 2025年 192卷

作者： He, Renchu Xie, Yunhao Zhang, Shiwei Xu, Feng Long, Jian China Univ Petr Coll Artificial Intelligence Dept Automat Beijing 102249 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China

As modern refinery crude oil scheduling scales up, traditional manual operations and independent optimization methods struggle with complexity and dynamics. This study proposes an empirically assisted integrated planning and scheduling optimization *** integrated model is solved using a hybrid optimization algorithm combining mathematical programming and particle swarm optimization (MP/PSO). Long-term planning aims to minimize operational and transportation costs while maximizing refinery profits;short-term scheduling, based on initial long-term plans, aims to minimize unit switchovers. In the short-term scheduling phase, heuristic rules based on empirical operational knowledge generate a high-performing initial population to accelerate convergence. This strategy is crucial for enhancing refinery emergency response capabilities, ensuring stable operations, and improving economic benefits. Experimental results show that within a reasonable time frame, MP/PSO performs better than PSO and manual scheduling in large-scale crude oil scheduling scenarios.

关键词： Crude oil scheduling Knowledge assisted Integrated planning and scheduling hybrid optimization algorithm

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