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5th International Conference on Big Data Analytics and Practices (IBDAP)

作者： Pravesjit, Sakkayaphop Kantawong, Krittika Kamkhad, Natdanai Sabaipom, Saksit Monchanuan, Jantawan Jitkongchuen, Duangjai Thammano, Arit Longpradit, Panchit Univ Phayao Sch Informat & Commun Technol Phayao Thailand Big Data Inst Bangkok Thailand King Mongkuts Inst Technol Ladkrabang Fac Informat Technol Ladkrabang Thailand Mahidol Univ Fac Social Sci & Humanities Salaya Nakhon Pathom Thailand

ISBN: (纸本)9798350391749

The proposed system represents an enhanced movement in search of food of whale optimization algorithm (WOA), based on Levy distribution for image classification of grape leaf disease. In the preprocessing, the proposed system uses convolution kernels to transform images into input data within the range of (0,1). Thereafter, the Levy distribution is incorporated into the WOA model as an exploration search mechanism. A grape leaf dataset from the Plant Village project (***), consisting of 4062 labeled images with dimensions of 256 by 256 pixels and divided into four classes -healthy, Black Rot, Black Measles, and Isariopsis leaf spot -is used to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than Visual Geometry Group (VGG16), Gray Level Co-occurrence Matrix (GLCM) with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and whale optimization algorithm (WOA).

关键词： whale optimization algorithm levy distribution convolution kernel grape leaf disease

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optimization of the Deployment of Wireless Sensor Networks Dedicated to Fire Detection in Smart Car Parks using Chaos whale optimization algorithm

Optimization of the Deployment of Wireless Sensor Networks D...

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IEEE International Conference on Communications (ICC)

作者： Benghelima, Slimane Charafeddine Ould-Khaoua, Mohamed Benzerbadj, Ali Baalat, Oumaya Ben-Othman, Jalel Univ Blida 1 Dept Informat LRDSI Ouled Yaich Algeria Ain Temouchent Univ Belhadj Bouchaib Temouchent Algeria Univ Oran 1 Ahmed Ben Bella Res Lab Ind Comp & Networks RIIR Es Senia Algeria Univ Bourgogne Franche Comte FEMTO ST Inst CNRS Belfort France Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst F-91190 Gif Sur Yvette France Univ Sorbonne Paris Nord Paris France Zayed Univ Coll Technol Innovat Dubai U Arab Emirates

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

Smart Car Parks (SCPs) based on Wireless Sensor Networks (WSNs) are one of the most interesting Internet of Things applications. This paper addresses the deployment optimization problem of two-tiered WSNs dedicated to fire monitoring in SCPs. Networks deployed inside the SCP consist of three types of nodes: Sensor Nodes (SNs) which cover the spots within the parking area, Relay Nodes (RNs) which forward alert messages generated by SNs, and the Sink node which is connected to the outside world (e.g, firefighters), through a high bandwidth connection. We propose an algorithm based on chaos theory and whale optimization algorithm (WOA), which minimizes simultaneously the deployed number of SNs, RNs, and network diameter while ensuring coverage and connectivity. To evaluate the effectiveness of our proposal, we have conducted extensive tests. The results show that the Chaos WOA (CWOA) outperforms the original WOA in terms of solution quality and computation time and by comparison with an exact method, CWOA provides results very close to the optimal in terms of fitness value and is efficient in terms of computational time when the problem becomes more complex.

关键词： whale optimization algorithm Chaos map WSN deployment Smart parking fire surveillance Internet of things

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whale optimization algorithm for Active Damping of LCL-Filter-Based Grid-Connected Converters

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2019年第2期9卷 986-996页

作者： Hato, Mohammed Mazen Bouallegue, Soufiene Univ Tunis El Manar Res Lab Automat Control LARA Natl Engn Sch Tunis ENIT BP 37 Tunis 1002 Tunisia

Grid-connected converters which are equipped with LCL-filters have drawn more and more attention due to their capability to accomplish a better harmonic reduction in comparison with the L-filters based ones. However, due to the resonance problem of the LCL-filters, stability issues may emerge in the current loop in case of the resonance peak is not fairly damped. Active damping strategy is preferred to the passive one due to the power losses on passive resistor and its additional cost. The capacitor current feedback active damping control approach, which is based on voltage oriented of Proportional Integral (PI), can be adopted as a good candidate to outperform the resonance hazard. Since the selection of PI controllers' parameters and capacitor current gains by iterative trials-errors based procedure becomes a time consuming and difficult task, an optimization problem under operating time-domain restrictions is modeled for tuning these decision variables. Such an optimization based-current feedback active damping control problem is managed by means of the advanced whale optimization algorithm (WOA). To evaluate the effectiveness of the introduced WOA metaheuristic, an empirical comparison study with the homologous Water Cycle algorithm (WCA), Grey Wolf Optimizer (GWO), Artificial Bee Colony (ABC) and Particle Swarm optimization methods is achieved. Statistical analysis is performed by using nonparametric Friedman's rank and Bonferroni-Dunn's test to check the significance of each algorithm. Demonstrative simulations highlight that the WOA method provides competitive solutions in terms of robustness and performance for the active damping controllers' tuning problem.

关键词： Active damping grid-connected converter LCL-filter PI tuning whale optimization algorithm Bonferroni-Dunn's and Friedman's tests

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whale optimization algorithm for Energy- Efficient Task Allocation in the Internet of Things

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第10期14卷 235-243页

作者： Yang, Shan Yu, Renping Jin, Xin State Grid Hubei Elect Power Co Ltd Wuhan 430077 Peoples R China Hubei Huazhong Elect Power Technol Dev Co Ltd Wuhan 430077 Peoples R China

Internet of Things (IoT) represents a new paradigm where various physical devices interact and collaborate to achieve common goals. This technology encompasses sensors, mobile phones, actuators, and other smart devices that work together to perform tasks and applications. In order to ensure optimal performance of these tasks and applications, task allocation becomes a critical aspect in IoT networks. Task allocation in IoT networks is a complex problem due to the intricate connections and interactions among devices. The task allocation issue is generally recognized as NP-hard, necessitating the development of effective optimization solutions. This paper proposes a solution using the whale optimization algorithm (WOA) to address the task allocation problem in IoT networks. By leveraging the capabilities of the WOA, our algorithm aims to improve energy efficiency and enhance network stability. The performance of the algorithm was tested comprehensively on the MATLAB simulation platform. Our algorithm outperforms existing algorithms in the literature, especially in terms of energy efficiency, as per the findings.

关键词： Task allocation internet of things energy efficiency optimization whale optimization algorithm

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Nonlinear predictive control based on echo state network and whale optimization algorithm 39

Nonlinear predictive control based on echo state network and...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Wang, Shuo Zhao, Xiyuan Li, Xuyue Zhou, YuTing Zhang, Danfeng Jin, Xin Liaoning Petrochem Univ Coll Informat & Control Engn Fushun Peoples R China

ISBN: (纸本)9798350390780;9798350379228

In response to the challenge of difficulty in modeling and low prediction accuracy for complex nonlinear processes, this paper proposes a nonlinear predictive control method that combines Echo State Networks and whale optimization algorithm. Firstly, an optimized Echo State Network is utilized to construct a predictive model, capturing the nonlinear dynamic characteristics of the system. Subsequently, this predictive model is integrated with model predictive control to achieve predictive control of the system. During the control process, the whale optimization algorithm is employed to solve the objective function in the rolling optimization loop, facilitating optimization adjustments to the control parameters. Finally, the proposed controller is applied to simulate and validate its effectiveness in a continuous stirred-tank reactor process. Simulation results demonstrate the efficacy of this approach.

关键词： Model Predictive Control Neural network Echo State Network Nonlinear system whale optimization algorithm

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Tuning of PID Controller using whale optimization algorithm for Different Systems

Tuning of PID Controller using Whale Optimization Algorithm ...

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IEEE International Conference on Digital Futures and Transformative Technologies (ICoDT2)

作者： Zaman, Uzair Khaleeq uz Naveed, Kanwal Kumar, Atal Anil Natl Univ Sci & Technol Coll Elect & Mech Engn Dept Mechatron Engn Rawalpindi Pakistan Univ Lorraine Arts & Metiers Inst Technol Lab Concept Fabricat Commande Metz France

ISBN: (纸本)9781665412858

In this paper, a recently developed meta-heuristic optimization method, whale optimization algorithm (WOA) is used to tune the parameters of PID controller for different systems. The algorithm is evaluated by setting integral absolute error and integral time absolute error as objective function for various transfer functions. The results obtained are compared with Ziegler-Nichols (Z-N), Genetic algorithm (GA) and Particle Swarm optimization (PSO). The results prove the effectiveness of the proposed algorithm and provides the researchers with options for improved system performance.

关键词： integral absolute error meta-heuristic algorithm PID controller whale optimization algorithm

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An Improved Binary whale optimization algorithm for Feature Selection of Network Intrusion Detection 4

An Improved Binary Whale Optimization Algorithm for Feature ...

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4th IEEE International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS)

作者： Xu, Hui Fu, Yingchun Fang, Ce Cao, Qianqian Su, Jun Wei, Siwei Hubei Univ Technol Wuhan 430068 Hubei Peoples R China CCCC Second Highway Consultants Co Ltd Wuhan 430052 Hubei Peoples R China

ISBN: (纸本)9781538675878

As for network intrusion detection, the time and space consumption of the detection algorithm increases with the feature dimension. The irrelevant or redundant features may cause the detection accuracy to decrease. In order to improve the timeliness and reliability of intrusion detection, feature selection is one of the most effective methods proposed currently. Considering that whale optimization algorithm (WOA) has good classification performance and dimensionality reduction in reducing feature redundancy, it can be applied to feature selection of network intrusion detection. The WOA converges slowly in the search process and easily falls into a local optimum in the updating mechanism, which restricts the classification performance and dimensionality reduction of the algorithm. Therefore, this paper adopts the nonlinear convergence factor strategy in the search process and introduces the Particle Swarm optimization (PSO) strategy in the updating mechanism. An Improved Binary whale optimization algorithm (IBWOA) is then proposed in order to achieve a better dimension reduction on the premise of ensuring accuracy for feature selection of intrusion detection. In the experimental stage, multiple data sets in the UCI database for machine learning were used for verification by comparing with other feature selection methods. Finally, the KDD CUP 99 data set was used for verification of feature selection in intrusion detection. The experimental results show that, compared with Genetic algorithm (GA), PSO and WOA, the IBWOA improves the classification accuracy and dimensionality reduction in feature selection of intrusion detection.

关键词： network intrusion detection feature selection whale optimization algorithm convergence factor Particle Swarm optimization

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Vehicle charging station location based on improved whale optimization algorithm 5

Vehicle charging station location based on improved whale op...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Li, Zhaodi Liu, Qingqing Yu, Kaixuan Henan Univ Dept Comp & Informat Engn Kaifeng Peoples R China Henan Light Ind Vocat Coll Dept Comp Sci & Art Zhengzhou Peoples R China State Key Lab Shield Machine & Boring Technol Zhengzhou Peoples R China

ISBN: (纸本)9798350386783;9798350386776

In order to better explore a method for solving the problem of electric vehicle charging station location planning, and improve the shortcomings of whale algorithm such as weak stability, sometimes slow convergence speed and easy to fall into local extremum, a whale optimization algorithm with quasi-opposition-based learning and enhanced search mechanism was proposed. Then, the algorithm flow is given, and the CEC2017 test function set is simulated by six representative algorithms in multiple dimensions. The results show that the convergence speed, optimization accuracy and solution stability of the improved algorithm are significantly improved, and it has good convergence performance. In order to solve the problem of electric vehicle public charging station location and service area division, an improved whale algorithm and Voronoi diagram are used to jointly solve the location planning of charging station, so as to achieve the purpose of global optimization. The application results of the last example show that the improved whale algorithm can effectively solve the charging station location problem and has superior optimization performance.

关键词： whale optimization algorithm Enhanced search Location of charging station

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A New Improved Multiple Behaviors whale optimization algorithm 7

A New Improved Multiple Behaviors Whale Optimization Algorit...

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7th International Conference on Natural Language Processing and Information Retrieval (NLPIR)

作者： Zhang, Yang Kanagawa Univ Dept Comp Sci Yokohama Kanagawa Japan

ISBN: (纸本)9798400709227

The whale optimization algorithm (WOA) has demonstrated superior performance compared with other swarm intelligence algorithms, making it widely used and applied to practical problemsolving. In this paper, an enhanced WOA is proposed, referred to as the Improved whale optimization algorithm (IWOA). The convergence factor of the traditional WOA is replaced with a random trigonometric function, which considers both the historical and current best positions of the whale population during the bubble-encircling search phase, thereby accelerating the global search speed of the whale population. In the random-encircling search phase, a combination of equiprobable random selection and roulette wheel selection is used to select random positions. Additionally, a random perturbation of individuals is introduced in the spiral search phase to prevent premature convergence to local optima. The effectiveness and robustness of the IWOA are evaluated using 4 benchmark function problems, and the results are compared with three other algorithms including particle swarm optimization (PSO). The experimental results demonstrate that the proposed IWOA outperforms other algorithms in terms of convergence speed and accuracy.

关键词： optimization Problem Swarm Intelligence algorithms Benchmark Function Problems whale optimization algorithm

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Fixed Head Short-term Hydrothermal Scheduling Using whale optimization algorithm Considering the Uncertainty of Solar Power 9

Fixed Head Short-term Hydrothermal Scheduling Using Whale Op...

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9th International Conference on Advanced Computing (ICoAC)

作者： Das, Sujoy Bhattacharya, Aniruddha Chakraborty, Ajoy Kumar Pandey, Vibhav Natl Inst Technol Agartala Dept Elect Engn Agartala 799046 Tripura India

ISBN: (纸本)9781538643495

In this article the authors have proposed the whale optimization algorithm to solve fixed-head short-term hydrothermal scheduling problem in the presence of solar powerconsidering multi-reservoir cascaded hydro plants. The generation of solar power mainly depends on the amount of solar irradiance, which is uncertain in nature. So, to deal with the uncertainty associated with solar energy, an efficient 2m point estimate method is used. To tackle the optimization part whale optimization algorithm is used. The hunting strategy of whales is very interesting and known as bubble-net feeding method. This strategy is very unique and mathematically modeled to perform an optimization process. To analyze the performance of the whale optimization algorithm, it has been tested on two test systems. The comparison of result shows that the proposed algorithm obtained better solution compare to other methods.

关键词： Hydrothermal scheduling Point estimate method Solar power whale optimization algorithm

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