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检索条件"主题词=Whale optimization algorithm"

共 1208 条记录，以下是311-320 订阅

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Optimal Allocation of Distributed Generation in A Part of The Egyptian Electrical Network Using whale optimization algorithm 21

Optimal Allocation of Distributed Generation in A Part of Th...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Zeuhom, Z. M. Boghdady, T. A. Abram Canadian Univ Giza Elect Power Engn Dept Giza Egypt Cairo Univ Elect Power Engn Dept Giza Egypt

ISBN: (纸本)9781728152899

The Distributed Generation (DG) relaying on generating units with small ratings to be linked into the distribution network close to the consumers. It can provide a promising future for power generation in electric networks. Recently, the demand for distributed generation into the electrical networks is rapidly increasing. Connecting DG units into the distribution networks can provide environmental, economic and technical merits. Those merits can be optimized if the DG unit site and size is properly determined. This paper presents a proposed multi-objective approach for determining the optimal allocation of the DG to enhance the voltage profile and minimizing the total active power loss of the distribution system. A recent optimization technique, whale optimization algorithm (WOA), is presented. A portion of the Egyptian electric network in the East Delta is introduced for testing the proposed algorithm via MatLab software.

关键词： Distributed generation Loss reduction Radial distribution network whale optimization algorithm

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Active Disturbance Rejection Control Tuned by whale optimization algorithm for Electric Loading System 39

Active Disturbance Rejection Control Tuned by Whale Optimiza...

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39th Chinese Control Conference (CCC)

作者： Chang, Jiaqi Shen, Dongkai Chen, Dong Beihang Univ Sch Automat Sci & Elect Engn Beijing 100083 Peoples R China Aviat Mil Representat Off Army Equipment Dept Che Chengdu 610000 Peoples R China

ISBN: (数字)9789881563903

ISBN: (纸本)9789881563903

The torque load test is one of the key tests in the development of aircraft sidestick. Due to the coupling effect of mechanical structure, the angle produced by aircraft sidestick itself will affected the loaded torque of electric loading system, so that the surplus torque appears. hi this paper, a control method is proposed to eliminate the surplus torque of loading device for aircraft sidestick. Active disturbance rejection control (ADRC) uses the extended state observer to collect all the nonlinear uncertainties for compensation. In order to avoid the negative influence of strong coupling of ADRC control parameters, whale optimization algorithm (WOA) is combined to automatically tune the parameters for the state of minimum surplus torque. The simulation results show that compared with PID control, the ADRC tuned by WOA can reduce the influence of sidestick angle disturbance, thus it satisfies the requirement for less surplus torque.

关键词： Active Disturbance Rejection Control whale optimization algorithm Surplus Torque Elimination Aircraft Sidestick

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R2-HMEWO: Hybrid multi-objective evolutionary algorithm based on the Equilibrium Optimizer and whale optimization algorithm

R2-HMEWO: Hybrid multi-objective evolutionary algorithm base...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Tahernezhad-Javazm, Farajollah Rankin, Debbie Coyle, Damien Ulster Univ Intelligent Syst Res Ctr Derry Londnderry North Ireland

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

Multi-objective evolutionary algorithms can be categorized into three basic groups: domination-based, decomposition-based, and indicator-based algorithms. Hybrid multi-objective evolutionary algorithms, which combine algorithms from these groups, are gaining increased popularity in recent years. This is because hybrid algorithms can compensate for the drawbacks of the basic algorithms by adding different operators and structures that complement each other. This paper introduces a hybrid-multi objective evolutionary algorithm (R2-HMEWO) that applies hybridization in the form of structure and operators. R2-HMEWO is based on the whale optimization algorithm (WOA) and equilibrium optimizer (EO). Elite individuals of WOA and EO are selected from a repository based on the R2-indicator and shifted density estimationbased method. In order to improve solutions' diversity, a reference points method is devised to select next-generation individuals. The proposed multi-objective algorithm is evaluated on 19 benchmark test problems (ZDT, DTLZ, and CEC009) and compared with six state-of-the-art (SOTA) algorithms (NSGA-III, NSGA-II, MOEA/D, MOMBI-II, MOEA/IGD-NS, and dMOPSO). Based on the inverted generational distance (IGD) metric (mean of 25 independent runs), R2-HMEWO outperformed other algorithms on 14 out of 19 test problems and revealed a highly competitive performance on the other test problems. Also, R2-HMEWO performed statistically significant better than MOEA/D and dMOPSO in 15/19 and 14/19 test problems, respectively (p<0.05), and reached significant performance in 4 test problems (from ZDT and CEC09) compared to other algorithms.

关键词： evolutionary algorithm multi-objective optimization whale optimization algorithm equilibrium optimizer reference directions R2 indicator shifted density estimation

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An Adaptive whale optimization algorithm Integrating Multiple Improvement Strategies 7

An Adaptive Whale Optimization Algorithm Integrating Multipl...

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7th IEEE International Conference on Advanced Robotics and Mechatronics

作者： Mai, Weifeng Mo, Liping Jishou Univ Sch Informat Sci & Engn Jishou 416000 Peoples R China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

A self-adaptive whale optimization algorithm integrating four improvement strategies is proposed to overcome the shortcomings of the basic whale optimization algorithm that are easy to fall into local optima, slow convergence speed and low calculation accuracy. Firstly, the chaotic logistics mapping method is used to initialize the population randomly to increase the diversity of the population and the uniformity of individual distribution. Secondly, the convergence factor is non-linearized and dynamically adjusted to balance the global search and local search performance of the algorithm. Thirdly, the dynamic adjustment of the search step length is realized by setting different adaptive inertia weights according to stages. Finally, crossover and mutation operations are performed on the random dimensions of the population individuals to avoid the algorithm from falling into the local optimum. The results of comparative experiments with three related algorithms on six benchmark functions show that the proposed algorithm is superior to the others in terms of global search capability, convergence speed and calculation accuracy.

关键词： swarm intelligent optimization algorithm whale optimization algorithm genetic algorithm chaotic mapping self-adaptation

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A Hybrid Spherical Search and whale optimization algorithm 13

A Hybrid Spherical Search and Whale Optimization Algorithm

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13th International Symposium on Computational Intelligence and Design (ISCID)

作者： Shi, Jiarui Yu, Jia Lee, Chiyan Todo, Yuki Gao, Shangce Univ Toyama Fac Engn Toyama 9308555 Japan Kanazawa Univ Fac Elect Informat & Commun Engn Kakuma Machi Kanazawa Ishikawa 9201192 Japan

ISBN: (纸本)9781728184463

In recent years, mixed-element heuristics have received increasing attention in optimization algorithms. The spherical search (SS)-a swarm-based meta-heuristic algorithm is used for solving global optimization problems with nonlinear constraints. The whale optimization algorithm (WOA), inspired by the bubble net hunting strategy, mimics the social behavior of humpback whales, whereas the heuristics method usually fall into a so-called "local optima trap". We proposed a hybrid algorithm based on the whale algorithm and the spherical search optimization hybrid algorithm, so that the two optimization strategies are merged into the presented algorithm which enables both algorithms to work in a co-evolutionary way. Experiment results show that the proposed hybrid algorithm can find the better solutions than some other new algorithms in term of convergence speed and solution accuracy.

关键词： sphere search whale optimization algorithm optimization meta-heuristics computational intelligence

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An Intrusion Detection Model Based on Improved whale optimization algorithm and XGBoost 11

An Intrusion Detection Model Based on Improved Whale Optimiz...

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11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Zong, Xinlu Li, Ruicheng Ye, Zhiwei Hubei Univ Technol Wuhan 430068 Peoples R China

ISBN: (纸本)9781665426053

Hyperparameters optimization is an effective way to improve the detection performance of intrusion detection models based on machine learning. However, hyperparameters optimization is a typical NP-hard problem. Therefore, it is difficult to find the optimal parameters within an acceptable time by using traditional parameters optimization methods. To solve this problem, this paper proposed an intrusion detection model named IWOA-XGB which combines improved whale optimization algorithm ( WOA) and eXtreme Gradient Boosting (XGBoost). Firstly, the spiral update maneuver of the original WOA is modified by setting the parameter l to a linearly decreasing random value. Then, the improved WOA is used to optimize several valued parameters of tree booster in the XGBoost. Finally, the optimized XGBoost is applied for intrusion detection so that the performance of the intrusion detection model can be improved. To evaluate the efficacy of IWOA-XGB, the NSL-KDD intrusion detection data set is used for simulation tests. The simulation results show that compared to the original XGBoost and other evolutionary algorithms, IWOA-XGB could effectively improve the macro F1-score of the detection model, demonstrating good intrusion detection performance.

关键词： Intrusion Detection Hyperparameter optimization XGBoost whale optimization algorithm

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Nonlinear Inertia Weight whale optimization algorithm with Multi-strategy and Its Application 19th

Nonlinear Inertia Weight Whale Optimization Algorithm with M...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Li, Cong Song Zou, Feng Chen, Debao Huaibei Normal Univ Inst Phys & Elect Informat Huaibei 235000 Peoples R China

ISBN: (纸本)9789819947546;9789819947553

whale optimization algorithm (WOA) suffers from slow convergence speed, low convergence accuracy, also difficulty in escaping local optima. To address these issues, we propose an improved whale algorithm that incorporates Latin hypercube sampling for population initialization. This ensures a more uniform distribution of the population in the initial stage compared to the random initialization. And then, introducing Cauchy Distribution intoWOA's searching prey stage. This prevents premature convergence to local optima and avoids affecting the later convergence. Furthermore, applying a nonlinear inertia weight to make an improvement on the convergence speed and accuracy of the algorithm. And compared the improved whale algorithm with other algorithms using 18 benchmark functions, and all results given indicated that the proposed algorithm better than original WOA and other algorithms. Finally, applying the improved WOA, original WOA and a mutate WOA to optimize the design of a pressure vessel, and the optimization results demonstrated that the effectiveness of the proposed method.

关键词： whale optimization algorithm Cauchy Distribution Nonlinear inertia weight Pressure vessel design

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Parameter Estimation of Software Reliability Growth Models by A Modified whale optimization algorithm 17

Parameter Estimation of Software Reliability Growth Models b...

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17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Lu, Kezhong Ma, Zongmin Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China Chizhou Univ Coll Comp Sci Chizhou 247100 Anhui Peoples R China

ISBN: (纸本)9781538674451

Software reliability growth models (SRGMs) are non-linear in nature, so they are difficult to estimate the proper parameters. An estimation method based on modified whale optimization algorithm (MWOA) in which parameters are estimated is discussed in this paper. The proposed MWOA shows significant advantages in handling variety of modeling problems such as the exponential model (EXPM), power model (POWM) and delayed S-shaped model (DSSM). However, the fitting error of a single model is relatively large. In view of the different growth characteristics at each stage of the model, a three-stage software reliability growth model is proposed in this paper. MWOA is used to estimate the parameters of three-stage software reliability models. Experimental results show that the fitting accuracy of three-stage model is significantly less than that of a single model. MWOA with the three-stage model can provide a better estimate of the software faults.

关键词： Software Reliability Growth Models Parameter Estimation whale optimization algorithm Three-stage Model

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Aero-engine Performance optimization Based on whale optimization algorithm 36

Aero-engine Performance Optimization Based on Whale Optimiza...

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第36届中国控制会议

作者： Xinge Huang Rui Wang Xudong Zhao Kaijian Hu School of Aeronautics and Astronautics Dalian University of Technology School of Control Science and Engineering Dalian University of Technology

ISBN: (纸本)9781538629185

Under the goal of pursuing safety,aero-engine is optimized by using an optimization algorithm to improve its performance and the comprehensive performance of the airplane.A nonlinear programming model is established to optimize the acceleration progress of a certain turbofan engine,where the rotor speed is involved in the objective ***,a novel nature-inspired optimization algorithm,called whale optimization algorithm（WOA）,which is used to solve the optimization problem of the *** results show that the WOA has a strong border search capacity and the engine’s acceleration capability has been improved.

关键词： Aero-engine optimization whale optimization algorithm

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Solving 0/1 Knapsack Problem using Opposition-based whale optimization algorithm (OWOA) 19

Solving 0/1 Knapsack Problem using Opposition-based Whale Op...

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8th International Conference on Software and Computer Applications (ICSCA)

作者： Alamri, Hammoudeh S. Zamli, Kamal Z. Ab Razak, Mohd Faizal Firdaus, Ahmad Univ Malaysia Pahang Fac Comp Syst & Software Engn Pahang Malaysia

ISBN: (纸本)9781450365734

The 0/1 Knapsack problem is one of the most popular real-world optimization problems that arise in searching space and finding the most optimum solution. Theoretically, the optimum solution problem of the 0/1 Knapsack requires suitable technique to explore the search space effectively. Practically, as many metaheuristic algorithms, whale optimization algorithm (WOA) may fail in local optimum solution. This paper proposes Opposition-based whale optimization algorithm (OWOA) to optimize solution problem in 0/1 Knapsack. The OWOA has been tested original WOA by using twenty cases of Knapsack problem and against other metaheuristic algorithms such as (CGMA) and HS-Jaya. The experimental results indicate a significant performance of the optimization solution and stabilization with minimal standard deviation value. This shows that the OWOA improved the original version WOA and has promising result in comparison with other existing algorithms.

关键词： 0\1 knapsack WOA whale optimization algorithm optimization Metaheuristic Real-World optimization Problem

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