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检索条件"主题词=Whale optimization algorithms"

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IFSWOA: Integrated Biomarker Selection Method Based on Incremental Feature Selection and whale optimization Algorithm

IFSWOA: Integrated Biomarker Selection Method Based on Incre...

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Computer Information Science and Application Technology (CISAT), International Conference on

作者： Yuanyuan Han College of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

In biological omics research, a notable characteristic of datasets is the overwhelming number of features compared to samples, posing significant challenges for data mining and model development. Feature selection algorithms serve as an effective approach to tackle this issue, aiming to identify the most representative biomarkers and consequently build predictive models with superior performance. However, the complexity and latent patterns within the feature selection solution space render the search for optimal feature subsets a daunting task. The Incremental Feature Selection (IFS) algorithm stands out in the field of feature selection due to its statistical underpinnings and excellent machine learning performance. Notably, different IFS algorithms exhibit complementary search capabilities when exploring feature subsets. This suggests that integrating multiple IFS algorithms can enable a more efficient exploration of the feature space, leading to the discovery of superior feature combinations. Drawing upon this insight, we introduce a novel feature selection method called IFSWOA. This method integrates three IFS-based feature selection algorithms and introduces an enhanced whale optimization Algorithm (WOA) to further refine the selected features. Through this approach, we significantly enhance the quality of the selected feature subsets and the predictive performance of the models. Experimental results demonstrate that the IFSWOA algorithm outperforms existing feature selection algorithms on a majority of omics datasets. This achievement not only validates the efficacy of IFS algorithms in biological omics feature selection, but also provides a novel and efficient feature selection strategy, opening new avenues for the analysis and mining of biological omics data.

关键词： Machine learning algorithms Biological system modeling Machine learning Predictive models Biomarkers Feature extraction Prediction algorithms whale optimization algorithms Data mining optimization

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Optimal Allocation of Multiple Distributed Generation Using an Enhanced whale optimization Algorithm

Optimal Allocation of Multiple Distributed Generation Using ...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： John Laurence P. Elambayo Adonis Emmanuel D C. Tio Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City Philippines

ISBN: (数字)9798350373929

ISBN: (纸本)9798350373936

The integration of distributed generation (DG) into the distribution system is a complex problem that requires an efficient optimization technique. To maximize benefits while minimizing disadvantages, it is crucial to determine optimal locations and capacities for DG installations. Despite the frequent use of recently developed optimization algorithms, their enhanced counterparts are rarely investigated. Moreover, most research relied on peak loads, which do not accurately reflect real-world demand. We proposed using the enhanced whale optimization algorithm (E-WOA) for optimal DG allocation in a radial distribution system, aiming to minimize power and energy losses. This study also developed representative variable load profiles based on real-world consumption data. The backward/forward sweep algorithm is used for load flow analysis, with the IEEE 33-Bus radial distribution system serving as the test system. The optimization was simulated over 100 independent runs, each with 200 iterations and a population of 50. Results demonstrated that E-WOA effectively solves the optimal DG allocation problem, achieving a power loss of 72.785 kW for three DGs at unity power factor, outperforming the standard WOA and several recent algorithms, and matching the performance of hybrid algorithms. Comparison with WOA across different loading scenarios showed that E-WOA consistently produced higher quality solutions at the cost of slower convergence speeds, longer runtimes, and poor scalability. These suggest E-WOA’s limitations in addressing large-scale optimization tasks.

关键词： Runtime Scalability Asia whale optimization algorithms Hybrid power systems Distributed power generation Resource management optimization Standards Convergence

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Trade-Off Planning of Electric Vehicle Charging Stations Based on Improved whale optimization Algorithm

Trade-Off Planning of Electric Vehicle Charging Stations Bas...

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New Energy and Electrical Technology (ISNEET), International Symposium on

作者： Lei Zheng Zhongpei Liu Zhiyong Zha Huan Xu Hanghan Liang Guangcheng He Information Center State Grid Hubei Information and Telecommunication Company Wuhan Hubei China Electronic Information School Wuhan University Wuhan Hubei China

ISBN: (数字)9798331505905

ISBN: (纸本)9798331505912

The surge in electric vehicles poses new challenges to charging stations, and a reasonable layout plan will promote the development of the electric vehicle industry. In the context of charging demand affected by seasonality, daily variations, we propose a method for the layout of electric vehicle charging stations calculated in granularity of hours. Firstly, based on the demand points after Monte Carlo simulation, we propose a dynamic time-varying charging station planning model with the optimization objectives of maximizing the annual revenue and minimizing user losses, under considering distance constraints, power constraints, queuing time constraints. To address this issue, we propose a two-stage search algorithm that combines random search with whale optimization Algorithm (RS-WOA) to find optimized solutions at both coarse and fine granularity levels. Finally, numerical simulation experiments were conducted in an example of the Optics Valley region in Wuhan, China. The experimental results show that our proposed algorithm achieved an annualized profit of 17.8399 million, which improved 5.3% and 37.7% compared to the RS algorithm and WOA algorithm, respectively.

关键词： Costs Heuristic algorithms Layout Charging stations whale optimization algorithms Electric vehicle charging Planning Time factors Vehicle dynamics optimization

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Depression Diagnosis using Restricted Crossover and Mutation Based whale optimization Algorithm

Depression Diagnosis using Restricted Crossover and Mutation...

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Computing, Sciences and Communications (ICCSC), International Conference on

作者： Prableen Kaur Ritu Gautam Manik Sharma Dept. of Computer Sci. And App. GMN College Ambala India Dept. of Computer Sci. And App. Amity University Noida India Dept. of Computer Sci. And App. DAV University Jalandhar India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

whale optimization algorithm (WOA) and Genetic Algorithm (GA) are very effective soft computing techniques. Numerous soft computing methods, such as evolutionary and swarm intelligence, have been used to identify the best features needed for the diagnosis of psychiatric diseases, such as depression, anxiety, and stress. A novel hybrid feature selection method called RCM-WOA, which is based on the principles of genetic algorithm with some restriction on crossover and mutation and whale optimization technique has been proposed as a solution to the feature selection problem. In addition, the outcomes of the suggested approach are contrasted with nine distinct soft computing methodologies.

关键词： Accuracy Sensitivity Error analysis Anxiety disorders Feature extraction Depression whale optimization algorithms Data mining Genetic algorithms Dementia

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optimization of cloud computing task scheduling based on whale optimization algorithm with quasi-opposition-based and nonlinear factor

Optimization of cloud computing task scheduling based on wha...

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Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Xiong Xuan Shaobo Li Liya Yu Fengbin Wu Xingxing Zhang Xue An State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Mechanical Engineering Guizhou University Guiyang China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

Task scheduling has a significant impact on the resource availability and operation cost of the system in cloud computing. In order to improve the efficiency of task execution in the cloud for a given computational resource, the improved whale optimization algorithm is applied to the multi-objective optimization model for cloud task scheduling. Specifically, the quasi-opposition-based learning strategy is added in the initiation phase to increase the diversity of the population. Meanwhile, a novel nonlinear convergence factor was introduced to augment the local search capability. Experimental results indicate that the improved whale optimization algorithm converges faster and searches with higher precision in finding the optimal task scheduling solution compared to existing meta-heuristic algorithms. In addition, the modified algorithm achieves superior system resource usage when dealing with both small and large tasks.

关键词： Cloud computing Costs Processor scheduling System performance optimization models Metaheuristics Scheduling whale optimization algorithms Internet of Things Convergence

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Calibration of Fractional-order Car-following Model Using Improved whale optimization Algorithm

Calibration of Fractional-order Car-following Model Using Im...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Yongchen Fu Wei Hu Yibing Cui Wenjuan Gu Rui Jiang Yongguang Yu School of Traffic and Transportation Beijing Jiaotong University Beijing China School of Systems Science Beijing Jiaotong University Beijing China School of Mathematics and Statistics Beijing Jiaotong University Beijing China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Intelligent driver model (IDM) is a famous car-following model in the traffic flow theory to illustrate the traffic phenomenon. While fractional-order calculus can precisely depict and predict various complex phenomena with the memory effect and nonlinear characteristics. Thus, this paper creatively proposes a fractional-order intelligent driver model (FIDM). Besides, to calibrate the model more accurately, we innovatively put forward a novel improved whale optimization algorithm, named as WOAFSC, fused with fractional-order strategy and stochastic center search strategy. Finally, the proposed FIDM and WOAFSC are validated on three data sets separately. Experimental results demonstrate their stronger robustness and higher accuracy. It should be noted that this research can provide theoretical support and practical guidance for fractional-order calculus in the fields of swarm intelligence optimization algorithms and car-following behavior modeling.

关键词： Accuracy Computational modeling Stochastic processes Search problems Calculus whale optimization algorithms Stability analysis Calibration optimization Convergence

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DNA-Encoded Design of Arithmetic optimization Algorithm Fused With whales

DNA-Encoded Design of Arithmetic Optimization Algorithm Fuse...

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Machine Intelligence Theory and Applications (MiTA), International Conference on

作者： Linpeng Xie Changjun Zhou Riheng Jia College of Computer Science and Technology Zhejiang Normal University Jinhua China

ISBN: (数字)9798350385670

ISBN: (纸本)9798350385687

DNA coding is a branch of DNA computation, and the quality of DNA coding is an important indicator that affects the accuracy of DNA computation, and how to optimize the quality of encoding and design a better DNA coding sequence is a problem that needs to be solved, and DNA encoding optimization refers to the selection of the optimal DNA code from the DNA coding set that satisfies the constraints. In this paper, the arithmetic optimization algorithm fused with whales is used to optimize the DNA sequence. The whale strategy is used to make up for the shortcomings of some arithmetic optimization algorithms. Through the DNA sequence optimization design, the obtained results are compared with those of predecessors, and most of the experimental results are better than those of predecessors, which further illustrates the feasibility of the algorithm.

关键词： Codes Accuracy DNA Gaussian distribution Encoding whale optimization algorithms Biological information theory optimization Machine intelligence Arithmetic

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Optimal Power Flow of Power Systems Using Hybrid whale and Particle Swarm optimization Technique

Optimal Power Flow of Power Systems Using Hybrid Whale and P...

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IEEE International Conference on Energy Internet (ICEI)

作者： Huanyou Wang Guangqi Xie Yingying Zhan School of Mechanical and Electrical Engineering Guangdong University of Science and Technology Dongguan China Academy of Electronic Information and Electrical Engineering Xiangnan University Chenzhou China

ISBN: (数字)9798331523558

ISBN: (纸本)9798331523565

In order to ensure population diversity and overcome premature convergence, differential evolution strategy was introduced and a whale particle swarm hybrid algorithm was proposed. The proposed method has been achievemented on the IEEE 30-bus test system considering three objective functions, such as line loss, generation cost, and weighted generation cost and voltage offset. By comparing whale particle swarm hybrid algorithm, whale optimization algorithm and particle swarm optimization algorithm, the experimental results show that whale particle swarm hybrid algorithm is more stable and better than the two other optimization algorithms in optimizing power flow.

关键词： Costs System verification Voltage whale optimization algorithms Hybrid power systems Particle swarm optimization MATLAB optimization Load flow Convergence

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Shared Energy Storage Capacity Allocation for Wind Farm Stations Based on whale optimization Algorithm

Shared Energy Storage Capacity Allocation for Wind Farm Stat...

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Power Science and Technology (ICPST), IEEE International Conference on

作者： Shuai Gao Weiqing Wang Engineering Research Center of Ministry of Education for Renewable Energy Generation and Grid Connection Technology Xinjiang University Urumqi China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Due to the singular nature of energy storage’s insufficient flexible and low economic efficiency difficulty in coping with the volatility and uncertainty of wind power grid integration. Therefore, a collaborative shared energy storage grid-connected system for wind farm stations is established to distribute shared energy storage capacity among multiple wind farm stations. Based on the error model of the actual output and planned output of multiple wind power stations, a deviation penalty mechanism is established to fix the price of energy trading and capacity leasing, and the optimal shared energy storage capacity is finally allocated. Through the utilization of the whale optimization Algorithm (WOA) to address the model, the proposed capacity allocation method’s rationality is validated. This method effectively reduces wind farm station costs by 9.96%, demonstrating the feasibility and continuity of the shared energy storage model.

关键词： Costs Uncertainty Collaboration Wind farms Wind power generation whale optimization algorithms Resource management

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An Improved Adaptive Beluga whale optimization for Global optimization

An Improved Adaptive Beluga Whale Optimization for Global Op...

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International Conference on Computational Intelligence and Applications (ICCIA)

作者： Xiaohui Shen Yonggang Wu School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

A multi-strategy improved beluga whale optimization (IBWO) is proposed to overcome the shortcomings of BWO, such as its susceptibility to local optimization and poor accuracy. In IBWO, three improvement strategies are introduced into the basic BWO. Firstly, the opposition-based initialization process provides the search procedure with good initial candidate solutions. Additionally, the double adaptive parameter approach provides a better balance between exploration and exploitation. Lastly, the combination of elite strategy with spiral search directs searchers to concentrate on important regions, consequently minimizing the amount of invalid search space. To test the scalability of IBWO, 18 benchmark functions in four different dimensions (30, 50, 100, 500) are tested and compared with GWO, HHO, WOA, SMA, PSO, GA, and MPA. In addition, two engineering design problems demonstrate the potential of IBWO to solve real-world optimization problems. The simulation results show that for most test problems, IBWO improves the optimization performance of BWO in terms of convergence accuracy and convergence speed.

关键词： Accuracy Spirals Simulation Scalability Benchmark testing Search problems whale optimization algorithms Robustness optimization Convergence

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