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检索条件"主题词=Whale optimization algorithm"

共 1208 条记录，以下是341-350 订阅

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Linear unequally spaced array synthesis for sidelobe suppression with different aperture constraints using whale optimization algorithm 13

Linear unequally spaced array synthesis for sidelobe suppres...

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13th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhang, Chengyan Fu, Xiongjun Peng, Shuilian Wang, Yi Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China CETC Res Inst 54 Shijiazhuang 050081 Hebei Peoples R China

ISBN: (纸本)9781538637586

In this paper, the whale optimization algorithm (WOA) is introduced and applied to the synthesis of uniformly excited broadside linear unequally spaced arrays for sidelobe suppression with different aperture constraints. Two design examples of pattern synthesis with fixed and unfixed array aperture respectively have been chosen to illustrate the effectiveness and superiority of WOA when compared to synthesis techniques described in literature. The results show that the WOA achieves lowest maximum sidelobe level relative to other state-of-the-art algorithms in two design cases. The WOA results and convergence tests demonstrate superior applicability of this novel optimization algorithm.

关键词： unequally spaced array whale optimization algorithm sidelobe suppression

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Automatic Shape Matching Using Improved whale optimization algorithm with Atomic Potential Function 18th

Automatic Shape Matching Using Improved Whale Optimization A...

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18th International Conference on Intelligent Computing (ICIC)

作者： Wei, Yuanfei Ling, Ying Luo, Qifang Zhou, Yongquan Guangxi Minzu Univ Xiangsihu Coll Nanning 530225 Guangxi Peoples R China Natl Univ Malaysia Fac Informat Sci & Technol Ukm Bangi 43600 Selangor Malaysia Guangxi Minzu Univ Sch Artificial Intelligence Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Nanning 530006 Peoples R China

ISBN: (纸本)9783031138324;9783031138317

This paper an improved whale optimization algorithm (IWOA) with atomic potential matching (APM) for shape matching is proposed. The optimization process of shape matching is considered as a numerical optimization problem which can be conventionally exploited by the optimization algorithms. We improved whale optimization algorithm (WOA) where our modifications add the local pollination phase from the flower pollination algorithm. Then the IWOA based atomic potential matching (APM) model for searching the optimal shape matching. The comparative experiments with other algorithms for solving three different examples of shape matching problems demonstrate the feasibility and effectiveness. Meanwhile, the proposed shape matching algorithm proved to be superior to the comparative others swarm intelligence optimization algorithms, improved quality of results in all examples and significantly improve convergence speed.

关键词： whale optimization algorithm Atomic potential matching Local pollination phase Shape matching Swarm intelligence optimization

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A Backbone whale optimization algorithm Based on Cross-stage Evolution 13th

A Backbone Whale Optimization Algorithm Based on Cross-stage...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Yang, Xin Wang, Limin Zhang, Zhiqi Han, Xuming Yue, Lin Jinan Univ Guangzhou Peoples R China

ISBN: (纸本)9783031096778;9783031096761

The swarm intelligent algorithms (SIs) are effective and widely used, while the balance between exploitation and exploration directly affects the accuracy and efficiency of algorithms. To cope with this issue, a backbone whale optimization algorithm based on cross-stage evolution (BWOACS) is proposed. BWOACS is mainly composed of three parts: (1) adopts the density peak clustering (DPC) method to actively divide the population into several sub-populations, generates the backbone representatives (BR) during backbone construction stage;(2) determines the deviation placement (DP) by constructing the co-evolution operators (CE), the search space expansion operators (SE) and the guided transfer operators (GT) during bionic evolution strategy stage;(3) realises the bionic optimisation through DP during backbone representatives guiding co-evolution stage. To verify the accuracy and performance of BWOACS, we compare BWOACS with other variants on 9 IEEE CEC 2017 benchmark problems. Experimental results indicate that BWOACS has better accuracy and convergence speed than other algorithms.

关键词： whale optimization algorithm Density peak clustering Bionic evolution strategy

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Long Short-Term Memory for Short Term Load Forecasting with Singular Spectrum Analysis and whale optimization algorithm 5

Long Short-Term Memory for Short Term Load Forecasting with ...

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5th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Zhang, Ruixiang Yuan, Meng Jin, Zhaorui Zhu, Ziyu Chen, Yuanhui Wang, Yu Sun, Yaojie Zhao, Longjun Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China Fudan Univ Inst Six Sect Econ Shanghai Peoples R China Fudan Univ Acad Engn & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Artificial Intelligence & I Shanghai Peoples R China

ISBN: (纸本)9781665490542

Short-term load forecasting is necessary for the safety and high efficiency of power system. In recent years, a large number of artificial intelligence-based methods have been used for short-term load forecasting by researchers, among which deep learning methods such as long short-term memory (LSTM) has been proved to be better at load forecasting. However, in the process of constructing LSTM models, data processing and hyperparameter selection tend to depend on experience, resulting in relatively slow modeling speed. This study proposes a short-term load forecasting method with singular spectrum analysis (SSA), LSTM network and whale optimization algorithm (WOA). SSA is used to decompose and reorganize the load sequence, and the WOA is used to optimize the hyperparameters of the LSTM network. The results show that the proposed method performs better than the benchmark in the three counties of the United States used for verification and comparison, that is, SSA-WOA-LSTM can be used to improve the accuracy of the short-term load forecasting.

关键词： short term load forecasting singular spectrum analysis whale optimization algorithm long short-term memory hyperparameter optimization

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An Enhanced whale optimization algorithm for the Two-Dimensional Irregular Strip Packing Problem 33rd

An Enhanced Whale Optimization Algorithm for the Two-Dimensi...

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33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Liu, Qiang Huang, Zehui Zhang, Hao Wei, Lijun Guangdong Univ Technol Guangdong Prov Key Lab Comp Integrated Mfg Syst State Key Lab Precis Elect Mfg Technol & Equipmen Guangzhou 510006 Peoples R China

ISBN: (纸本)9783030557881;9783030557898

In this paper, we propose an enhanced whale optimization algorithm for the two-dimensional strip packing problem, which requires cutting a given set of polygons from a sheet with fixed-width such that the used length of the sheet is minimized. Based on the original whale swarm algorithm, this algorithm introduces strategies such as adaptive weighting factors, local perturbation, and global beat variation, which can better balance the global optimization and local optimization search capabilities of the whale algorithm. The proposed algorithm was tested using the standard test case and compared with other algorithms in the literature. The results show that the proposed algorithm can improve the best-known solution for some instances.

关键词： Irregular packing whale optimization algorithm Local search

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Multi-Mechanism-Driven whale optimization algorithm for Collaborative Scheduling of Industrial Equipment 14

Multi-Mechanism-Driven Whale Optimization Algorithm for Coll...

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14th Asian Control Conference (ASCC)

作者： Lin, Bin Wang, Yalin Tan, Xujie Guan, Lin Qiu, Zhifeng Deconinck, Geert Liu, Chenliang Cent South Univ Sch Automat Changsha Peoples R China Natl Engn Res Ctr Adv Energy Storage Mat Changsha Peoples R China Katholieke Univ Leuven Natl Engn Res Ctr Adv Energy Storage Mat B-3001 Leuven Belgium

ISBN: (纸本)9798331540845;9789887581598

Cranes are typical industrial equipment frequently used in process industries. As the scale of cranes continues to grow, the complexity of their collaborative scheduling problems becomes increasingly prominent. However, traditional optimization algorithms cannot solve these problems well. The dynamic balancing of global search and local exploitation and sensitivity to local optimal solutions in collaborative scheduling problems of industrial equipment present significant hurdles. Hence, this paper proposes a multi-mechanism-driven whale optimization algorithm (MMD-WOA) for establishing a collaborative scheduling model involving multiple cranes operating on the same track. First, a dynamic inertia weight coefficient is devised to balance global search and local exploitation based on environmental cues. Then, a Levy mutation mechanism is integrated to augment the population's capability to escape local optima. After that, a boundary solution repair mechanism is formulated to enhance the utilization and diversity of individual populations. Finally, the effectiveness of the proposed MMD-WOA is validated across 23 benchmark functions and an industrial scheduling problem. The experimental results demonstrate the superior global optimal search performance and robustness of the proposed method.

关键词： Process industry collaborative scheduling whale optimization algorithm dynamic inertia weight coefficient Levy mutation mechanism

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A Spark-based Distributed whale optimization algorithm for Feature Selection 10

A Spark-based Distributed Whale Optimization Algorithm for F...

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10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Chen, Hongwei Hu, Zhou Han, Lin Hou, Qiao Ye, Zhiwei Yuan, Jiansen Zeng, Jun Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Wuhan Fiberhome Tech Serv Co Ltd Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781728140698

With the rapid development of the Internet and big data technologies, high-dimensional data generated in various fields has increased dramatically. Feature selection is an effective way to solve data processing problems caused by high dimensionality and high computational complexity. The traditional feature selection method shows the problem of insufficient classification accuracy and low processing efficiency when dealing with high-dimensional and large-scale data. The traditional feature selection method shows low classification accuracy and low processing efficiency when dealing with high-dimensional and large-scale data. This paper proposed a feature selection method based on whale optimization algorithm to learn mining feature selection rules, then improve the accuracy of feature selection. However, when the data size is very large, the efficiency of single node execution is low. Therefore, this paper combined the whale optimization algorithm with the parallel computing model of the Spark platform, and proposed a feature selection method based on the Spark platform for distributed whale optimization algorithm. The results showed that the excellent result search ability of the whale optimization algorithm combined with the distributed and efficient calculation speed can realize the efficient solution of the feature selection optimization model.

关键词： feature selection whale optimization algorithm Spark platform parallel computing

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Optimal Selection of Voltage Sag Mitigating Devices using whale optimization algorithm for Small and Medium Sized Customers in Distribution System 2

Optimal Selection of Voltage Sag Mitigating Devices using Wh...

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2nd International Conference on Energy, Power and Environment - Towards Smart Technology (ICEPE)

作者： Mitra, Rituparna Gope, Sadhan Goswami, Arup Kumar Tiwari, Prashant Kumar Natl Inst Technol Silchar Dept Elect Engn Silchar Assam India Mizoram Univ Dept Elect Engn Aizawl India

ISBN: (纸本)9781538647691

Small and medium sized customers emerge and plays a pivotal role in modern times due to rapid urbanization. Small and medium customers are constituted by domestic user, big malls, hospitals, educational institutions, medium and small sized traders. This medium and tiny sector deserves uninterrupted hassle free and quality power for their daily run. Interruption in power distribution affects distributors' image, revenue lost (cost incurred for production of power is not recovered from clientele) can be eradicated by using the concept of placing voltage sag mitigating devices. Optimal selection of voltage sag mitigating devices is based on the parameters like capacity, size, cost and load factors (for specific placement of devices). whale optimization algorithm has been applied as tool. Loss of energy/power is avoided culminating save of energy and economic gain.

关键词： Distribution System Voltage Sag Voltage Sag Mitigating Devices whale optimization algorithm

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A Multi-Objective Optimal Vehicle Fuel Consumption Based on whale optimization algorithm 12th

A Multi-Objective Optimal Vehicle Fuel Consumption Based on ...

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12th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP)

作者： Horng, Mong-Fong Thi-Kien Dao Shieh, Chin-Shiuh Trong-The Nguyen Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung Taiwan Haiphong Private Univ Dept Informat Technol Haiphong City Vietnam

ISBN: (纸本)9783319502120;9783319502113

Due to the complex environmental constraints in the field of transport and logistics, optimization for the vehicle fuel consumption problem satisfies not only one criterion but also several criteria. In this paper, a novel multi-objective method for an optimal vehicle traveling based on whale optimization algorithm (WOA) is proposed. In the proposed method, two criteria of distance and path-travel gasoline of the vehicle traveling issue are transformed into a minimization one. The vehicle routing and traffic status in the environmental transportation are considered to model the fitness function for the solution in WOA. The path of the globally best whale in each iteration is selected, and reached by the vehicles in sequence. In addition, the information of traffic status updates to vehicle periodically times from traffic navigation system during the traveling. Series scenarios of simulations are implemented in different traffic environments for the optimal paths when the vehicles reached the destination. The results show that the proposed method provides the confirm the practicality of the model of transport and logistics, and this proposed method may be the alternative method of optimization for the vehicle traveling in the logistics.

关键词： whale optimization algorithm Vehicle fuel consumption Multi-objective optimization

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Tuning of SMC Parameters for Robotic Manipulator Based on whale optimization algorithm 2

Tuning of SMC Parameters for Robotic Manipulator Based on Wh...

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2nd World Robot Conference (WRC) / Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Du, Meilin Guo, Zuhua Meng, Cai Beihang Univ Sch Astronaut 37 Xueyuan Rd Beijing Peoples R China

ISBN: (纸本)9781728155524

A 2-link robotic manipulator is selected as a research instance, a sliding mode control(SMC) law is designed. Parameters tuning of sliding mode controller is converted to a nonlinear optimization problem with the objects of minimizing the trajectory tracking error and eliminating chattering of the control torques. The whale optimization algorithm (WOA) is adopted in this paper to obtain the best parameters of controller, and a comparative study with the results that are searched by particle swarm optimization (PSO) is made. Through simulation of the manipulator, control effects of the parameters obtained by the two optimization methods are compared. Research shows that WOA is a viable optimization method for parameter tuning of sliding mode control of robotic manipulator, and the optimization model proposed in this paper can effectively eliminate the chattering problem of control torques.

关键词： whale optimization algorithm Sliding Mode Control Robotic Manipulator Control Nonlinear optimization

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