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Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO algorithm

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ENERGIES 2018年第9期11卷 2385页

作者： Wang, Meihua Yeh, Wei-Chang Chu, Ta-Chung Zhang, Xianyong Huang, Chia-Ling Yang, Jun South China Agr Univ Coll Math & Informat Guangzhou 510633 Guangdong Peoples R China Natl Tsing Hua Univ Dept Ind Engn & Engn Management Integrat & Collaborat Lab Hsinchu 300 Taiwan Southern Taiwan Univ Sci & Technol Dept Ind Management & Informat Tainan 700 Taiwan Guangdong Polytech Normal Univ Dept Automat Guangzhou 510633 Guangdong Peoples R China Kainan Univ Dept Logist & Shipping Management Taoyuan 33857 Taiwan South China Univ Technol Sch Math Guangzhou 510633 Guangdong Peoples R China

Wireless (smart) sensor networks (WSNs), networks made up of embedded wireless smart sensors, are an important paradigm with a wide range of applications, including the internet of things (IoT), smart grids, smart production systems, smart buildings and many others. WSNs achieve better execution efficiency if their energy consumption can be better controlled, because their component sensors are either difficult or impossible to recharge, and have a finite battery life. In addition, transmission cost must be minimized, and signal transmission quantity must be maximized to improve WSN performance. Thus, a multi-objective involving energy consumption, cost and signal transmission quantity in WSNs needs to be studied. Energy consumption, cost and signal transmission quantity usually have uncertain characteristics, and can often be represented by fuzzy numbers. Therefore, this work suggests a fuzzy simplified swarm optimization algorithm (fSSO) to resolve the multi-objective optimization problem consisting of energy consumption, cost and signal transmission quantity of the transmission process in WSNs under uncertainty. Finally, an experiment of ten benchmarks from smaller to larger scale WSNs is conducted to demonstrate the effectiveness and efficiency of the proposed fSSO algorithm.

关键词： smart sensor network wireless smart sensor network fuzzy energy consumption activity on arc swarm intelligence algorithm

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Optimized levy flight model for heart disease prediction using CNN framework in big data application

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 223卷

作者： Jain, Arushi Rao, Annavarapu Chandra Sekhara Jain, Praphula Kumar Hu, Yu-Chen Indian Inst Technol Indian Sch Mines Dept Comp Sci & Engn Dhanbad JH India GLA Univ Dept Comp Engn & Applicat Mathura 281406 India Providence Univ Dept Comp Sci & Informat Management Taichung Taiwan

Cardiac disease is one of the most complex diseases globally. It affects the lives of humans critically. It is essential for accurate and timely diagnosis to treat heart failure and prevent the disease. In most aspects, it was not so successful with the traditional method, which uses past medical history. Many existing models had several types of the loss function in traditional CNN can lead to misidentification of the model. To solve this problem, so many scholars have used the swarm intelligence algorithm, but most of these techniques are stuck in the local minima and suffer from premature convergence. In the proposed method, we build up the Levy Flight - Convolutional Neural Network (LV-CNN) depending on the diagnostic system using heart disease image data set for heart disease assessment. Initially, the input Big Data images are resized to reduce the computational complexity of the system. Then, those resized images are subject to the proposed LV-CNN model. Therefore, the LV approach is integrated with the Sunflower Optimization algorithm (SFO) to reduce loss function occurring in the CNN architecture. Such a combination helps the SFO algorithm avoid trapping in local minima due to the random walk of the levy flight. The proposed algorithm will be simulated using the MATLAB tool and tested experimentally in terms of accuracy is 95.74%, specificity is 0.96%, the error rate is 0.35, and time consumption is 9.71 s. This comparative analysis revealed that the excellence of the proposed model.

关键词： Optimization Heart disease prediction Convolution neural networks Big data swarm intelligence algorithm

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GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems

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JOURNAL OF COMPUTATIONAL SCIENCE 2022年第0期61卷

作者： Nadimi-Shahraki, Mohammad H. Taghian, Shokooh Mirjalili, Seyedali Zamani, Hoda Bahreininejad, Ardeshir Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Teknol Brunei Fac Engn Bandar Seri Begawan Brunei

In this article, an improved variant of the grey wolf optimizer (GWO) named gaze cues learning-based grey wolf optimizer (GGWO) is proposed. The main intentions are to reduce the existing high selective pressure and low diversification of the GWO algorithm, which results in premature convergence, local optima trapping, and stagnation problems. The GGWO algorithm benefits from two new search strategies: neighbor gaze cues learning (NGCL) and random gaze cues learning (RGCL) inspired by the gaze cueing behavior in wolves. The NGCL strategy enhances the exploitation ability and local optima avoidance. The RGCL, however, boosts the population diversity and balance between exploration and exploitation. The cooperation among three search strategies GWO, NGCL, and RGCL, improves diversification, exploration, and exploitation. The GGWO algorithm performance was evaluated by conducting CEC'18 test functions. Furthermore, the results of GGWO were compared with nine metaheuristic algorithms KH, iwPSO, WOA, GWO, GWO-EPD, HGWOSCA, EEGWO, BOA, and VAGWO. Moreover, the experimental results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. Additionally, four real engineering design problems and two problems of optimal power flow (OPF) for the IEEE 30-bus and IEEE 118-bus are optimized to verify the applicability of the GGWO in practice. The results show that the GGWO algorithm has been able to provide competitive and superior results to the compared algorithms, and it is capable of solving engineering problems.

关键词： Grey wolf optimizer swarm intelligence algorithm Metaheuristic Optimization Engineering optimization problems Benchmark GWO

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Optimized strategy for DFIG wind farm considering turbine fatigue

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 123卷 106313-106313页

作者： Rong, Fei Xu, Xiaoyue Zhou, Shijia Huang, Ying Yang, Guangyuan Hunan Univ Dept Elect & Informat Engn Changsha 410082 Peoples R China China Southern Power Grid Elect Power Res Inst Guangzhou 510620 Peoples R China China Southern Power Grid EHV Maintenance & Test Guangzhou 510620 Peoples R China

In wind farms, the fatigue of a wind turbine reduces its life cycle. Additionally, when turbines are subjected to non-uniform fatigue loads, they tend to fail at certain high fatigue loads, which weakens the reliability of the wind farm and increases operating running costs. To operate the wind farm economically, the concept of equivalent fatigue of wind turbines is proposed, based on which an optimal strategy of active and reactive power dispatching of the wind farm is established. This considers the active power output of the wind farm and the fatigue of the wind turbines simultaneously. The swarm intelligence algorithm is used to solve the global optimal problem. Focusing on the profits of the wind farm, the reference values of optimal active and reactive output power of each turbine are calculated. Simulation results under the condition of free output and limited output verify that the proposed strategy can reduce the fatigues of wind turbines and decrease the losses of the wind farm. Thus, the life span of each wind turbine is extended, and the wind farm profits are increased.

关键词： Wind farm Fatigue Active and reactive power optimization swarm intelligence algorithm

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Path planning of multiple UAVs with online changing tasks by an ORPFOA algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL intelligence 2020年 94卷 103807-103807页

作者： Li, Kun Ge, Fawei Han, Ying Wang, Yi'an Xu, Wensu Bohai Univ Coll Engn Jinzhou 121013 Liaoning Peoples R China

The unmanned aerial vehicle (UAV) is a new type oilfield inspection tool which is characterized by high flexibility, low cost and high efficiency. In the UAV based oilfield inspection technology, the path planning is an indispensable element which finds an optimal flight path for UAV to finish the inspection jobs successfully. In comparison with the other researches, our study focuses on two challenging issues: path planning of multiple UAVs by traversing a certain amount of task points in the three-dimensional environment within the required completion time, and optimizing solving for the best flight path with online changing tasks. In the research, a novel task assignment method including the initial task assignment and the task assignment with changing tasks is proposed to determine the initial task sequences of each UAV and rapidly replan task sequences after tasks change. An improved fruit fly optimization algorithm (named ORPFOA) is proposed to solve the path planning problem in both initial task sequences and new task sequences after tasks change, in which the optimal reference point and a distance cost matrix are used to reach both faster solving and higher optimizing precision for the optimal flight path. In ORPFOA, two cost functions are defined to evaluate the optimizing results in the initial phase and the new phase after task changes, respectively. A simulation model of the three-dimensional oilfield environment is established to verify the effectiveness of the proposed method in comparison with other six algorithms.

关键词： Oilfield inspection UAVs Path planning Task assignment swarm intelligence algorithm Fruit fly optimization algorithm

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Towards the uncertainty quantification of semi-empirical formulas applied to the added resistance of ships in waves of arbitrary heading

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OCEAN ENGINEERING 2022年 251卷 111040-111040页

作者： Mittendorf, Malte Nielsen, Ulrik D. Bingham, Harry B. Liu, Shukui Tech Univ Denmark DTU Mech Engn DK-2800 Lyngby Denmark Nanyang Technol Univ Sch Mech & Aerosp Engn SG-639798 Singapore Singapore NTNU AMOS Ctr Autonomous Marine Operat & Syst NO-7052 Trondheim Norway

The present paper examines a semi-empirical framework for the estimation of added resistance in arbitrary wave heading under consideration of uncertainty quantification. In this respect, the calibration of the formula's parameter vector is conducted based on particle swarm optimization as well as a database of model test results comprising 25 different ships and around 1100 samples. In the first iteration, the minimization of reducible systematic uncertainty is of interest and the effect of four objective functions on prediction accuracy is evaluated. Moreover, two different parameter combinations were obtained for blunt (C-B >= 0.70) and slender type ships. Conversely, the irreducible statistical uncertainty, i.e. the inherent noise of the experimental data, is taken into account by a quantile regression procedure. Applying this approach, a 90% prediction interval for the formula's estimates is implemented using the skewed version of the superior loss function in the previous iteration. The practical relevance of an uncertainty estimate for the prediction of the added resistance is emphasized in the final part, in which the proposed approach is validated in regular waves against model test data and other well-established prediction methods. In general, the validation studies suggest satisfactory performance and reliability of the adapted semi-empirical formulation.

关键词： Added resistance Semi-empirical formula Uncertainty quantification Parameter calibration swarm intelligence algorithm

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Effective detection of Alzheimer's disease by optimizing fuzzy K-nearest neighbors based on salp swarm algorithm

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期159卷 106930-106930页

作者： Lu, Dongwan Yue, Yinggao Hu, Zhongyi Xu, Minghai Tong, Yinsheng Ma, Hanjie Wenzhou Univ Sch Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Univ Technol Sch Intelligent Mfg & Elect Engn Wenzhou 325035 Peoples R China Wenzhou Univ Intelligent Informat Syst Inst Wenzhou 325035 Peoples R China Key Lab Intelligent Image Proc & Anal Wenzhou Peoples R China

Alzheimer's disease (AD) is a typical senile degenerative disease that has received increasing attention worldwide. Many artificial intelligence methods have been used in the diagnosis of AD. In this paper, a fuzzy k-nearest neighbor method based on the improved binary salp swarm algorithm (IBSSA-FKNN) is proposed for the early diagnosis of AD, so as to distinguish between patients with mild cognitive impairment (MCI), Alzheimer's disease (AD) and normal controls (NC). First, the performance and feature selection accuracy of the method are validated on 5 different benchmark datasets. Secondly, the paper uses the Structural Magnetic Resolution Imaging (sMRI) dataset, in terms of classification accuracy, sensitivity, specificity, etc., the effectiveness of the method on the AD dataset is verified. The simulation results show that the classification accuracy of this method for AD and MCI, AD and NC, MCI and NC are 95.37%, 100%, and 93.95%, respectively. These accuracies are better than the other five comparison methods. The method proposed in this paper can learn better feature subsets from serial multimodal features, so as to improve the performance of early AD diagnosis. It has a good application prospect and will bring great convenience for clinicians to make better decisions in clinical diagnosis.

关键词： Feature selection Salp swarm algorithm Medical diagnosis swarm intelligence algorithm

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Big data based analytic model to predict and classify breast cancer using improved fractional rough fuzzy K-means clustering and labeled ensemble classifier algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第10期34卷

作者： Srikanth, K. Ul Huq, S. Zahoor Kumar, A. P. Siva JNTUA Dept Comp Sci & Engn Ananthapuramu Andhra Pradesh India G Pulla Reddy Engn Coll Autonomous Dept Comp Sci & Engn Kurnool Andhra Pradesh India

Breast cancer is a very dangerous disease that mainly affects women. It is a deadliest disease that highly affects the women's life. Therefore, it is necessary to predict and classify this deadly disease for early diagnosis. There exist numerous data mining techniques for early prediction and classification of this disease. The big data based analytical model provides the better solution for storing, manipulating, and analyzing a great number of mammographic images. In this article, a new improved fractional rough fuzzy K-means clustering strategy is considered for disease prediction. Then, a new Tunicate swarm algorithm (TSA) is introduced to optimize the weight parameters. TSA is a bio-inspired metaheuristic optimization approach. Finally, the labeled ensemble classifier (LEC) is utilized for classifying the stages of breast cancer as malignant and benign. Here, the data is randomly generated from breast cancer Wisconsin dataset (diagnosis) obtainable on UCI machine learning repository. The proposed strategy is compared with different existing strategies, like Logistic Regression Classifier, Random Forest Classifier. From the analysis, it is observed that the proposed big data based analytical model using LEC provides 99.3% accuracy that is very high when compared to the accuracy of existing approaches.

关键词： big data breast cancer data mining ensemble classifier K-means clustering swarm intelligence algorithm

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Study on Economic Dispatch of the Combined Cooling Heating and Power Microgrid Based on Improved Sparrow Search algorithm

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ENERGIES 2022年第14期15卷 5174-5174页

作者： Qiao, Mengmeng Yu, Zexu Dou, Zhenhai Wang, Yuanyuan Zhao, Ye Xie, Ruishuo Liu, Lianxin Shandong Univ Technol Sch Elect & Elect Engn Zibo 255000 Peoples R China

The reasonable and efficient use of the abundant biomass resources in rural areas has not been realized. Therefore, the concept of a combined cooling, heating, and power (CCHP) microgrid system, considering biomass pyrolysis and gasification, has been developed by researchers. A biomass gasification device can fully use biomass resources and can play a role in absorbing wind energy. Meanwhile, in order to minimize the operating cost of each micropower supply unit, as well as the environmental pollution costs, researchers have also established an optimal scheduling model for CCHP microgrids, which uses the sparrow search algorithm. In this paper, we have improved upon the traditional sparrow algorithm to solve the problems of its uneven population distribution, poor global search ability, and the risk of falling into local optima, through the development of the random walk sparrow search algorithm (RSSA). First, a sinusoidal chaotic map is used to generate the early-generation sparrow population with a uniform distribution in space. Second, in this study we add a sharing factor to the discoverer's optimization process to enhance information sharing and the global research capability among individuals in this field. Finally, a random walk strategy is used to form new participants to improve the algorithm's skill in locally searching for optimal locations. Taking the CCHP microgrid with grid-connected action as a case study, we concluded that compared with the optimization outcomes of the SSA, the total costs incurred by RSSA in summer and winter were reduced by 2.2% and 3.1%, respectively. Compared with the optimization findings for the chaotic SSA algorithm, the total costs incurred using the RSSA algorithm under typical summer and winter days were reduced by 0.14% and 0.13%, respectively. The productiveness of the RSSA algorithm for solving the CCHP microgrid economic dispatch issues has thus been verified.

关键词： microgrids power generation dispatch combined cooling heating and power (CCHP) system swarm intelligence algorithm optimization

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Metaheuristics for the positioning of 3D objects based on image analysis of complementary 2D photographs

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MACHINE VISION AND APPLICATIONS 2021年第5期32卷 105-105页

作者： Flori, Arnaud Oulhadj, Hamouche Siarry, Patrick Univ Paris Est Creteil LISSI F-94400 Vitry Sur Seine France

Today, advances in 3D modeling make it possible to identically reproduce objects, animals, humans and even entire scenes. The broad applications concern video games, virtual reality or augmented reality and cinema, for example. In this article, we propose a new method to build a 3D scene directly from several complementary photographs. The positions of the objects for which we already have a 3D model will be determined by triangulation, thanks to the information extracted from the photographs, such as the outline of the objects on the images. Each pixel of the images is converted into a value that gives its distance to the nearest outline. The 3D model of the objects is then projected on the converted images, and the triangulation is done using a cost function that gives the distance of each projection of the objects to their respective outlines. A projection is considered perfect when its distance to its outlines is null, which means that the cost function gives a score of zero as well. We propose to solve this optimization problem by means of two algorithms, namely Simulated Annealing (SA) and quantum particle swarm optimization (QUAPSO).

关键词： Simulated Annealing Particle swarm optimization swarm intelligence algorithm Auto-adaptive algorithm 3D model

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