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An improved cuckoo search optimization algorithm with genetic algorithm for community detection in complex networks

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第18期81卷 25205-25231页

作者： Shishavan, Saeid Talebpour Gharehchopogh, Farhad Soleimanian Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

This paper improved cuckoo search optimization (CSO) algorithm with a Genetic algorithm (GA) for community detection in complex networks. CSO algorithm has problems such as premature convergence, delayed convergence, and getting trapped in the local trap. GA has been quite successful in terms of community detection in complex networks to increase exploration and exploitation. GA operators have been used dynamically in order to increase the speed and accuracy of the CSO. The number of populations is dynamically adjusted based on the amount of exploration and exploitation. Modularity objective function (Q) and Normalized Mutual Information (NMI) is used as an optimization function. It was carried out on six types of real complex networks. The proposed algorithm was tested with GA, Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), and CSO, with different iterations in modularity and NMI criteria. The results show that in most comparisons, the proposed algorithm has been more successful than the basic comparative algorithms, and it has proven its superiority in terms of modularity and NMI. The proposed algorithm performed an average of 54% better in modularity and 88% in NMI than other algorithms. It performed on average in modularity criteria 84.3%, 58.8%, 33.7% and 38.8%, respectively, compared to CSO, ABS, GWO and GA algorithms, and in terms of NMI index, 188.7%, 39.1%, 52.3% and 73.8%, respectively in CSO, ABS, GWO and GA algorithms performed better.

关键词： cuckoo search optimization algorithm Genetic algorithm Community detection Complex networks

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A novel hybrid approach based on cuckoo search optimization algorithm for short-term wind speed forecasting

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ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2017年第3期36卷 943-952页

作者： Zhang, Kequan Qu, Zongxi Wang, Jianzhou Zhang, Wenyu Yang, Feiyue Lanzhou Univ Coll Atmospher Sci Key Lab Arid Climat Change & Reducing Disaster Ga Lanzhou Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

A type of renewable energy, wind power, which has a large generating capacity, has increasingly captured the world's attention. Forecasting wind speed is of great significance in wind-related engineering studies, the planning and designing of wind packs, wind farm management and the integration of wind power into electricity grids. Because of the chaotic nature and intrinsic complexity of wind speed, reducing forecasting errors related to wind speed has been an important research subject. This article proposes a hybrid forecasting model based on the wavelet neural network (WNN) method and ensemble empirical mode decomposition (EEMD) de-noise technology. Moreover, the cuckoo search optimization (CSO) algorithm is employed to optimize the parameters of the WNN model to avoid the over-fitting problem and improve fitting accuracy. A case study is performed to forecast 10-min wind speed data for wind farms in the coastal areas of China, and a comparison of the forecasting results with other models demonstrates that the proposed model can provide desirable forecasting results and improve forecasting accuracy. (c) 2017 American Institute of Chemical Engineers Environ Prog, 36: 943-952, 2017

关键词： wind speed prediction hybrid model cuckoo search optimization algorithm neural network

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PNasFH-Net: Pyramid neural architecture search forward network for facial emotion recognition in uncontrolled and pose variant environment

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KNOWLEDGE-BASED SYSTEMS 2025年 310卷

作者： Ravindran, Saranya Rajagopalan, Sasikumar RMK Coll Engn & Technol Dept Comp Sci & Engn Puduvoyal 601206 Tamil Nadu India RMD Engn Coll Dept Comp Sci & Engn Kavaraipettai Tamil Nadu India

Face emotion recognition has attracted more attention in recent years because of its wide range of applications. This framework represents a novel facial emotion recognition technique known as a Pyramid Neural Architecture search Forward Network (PNasFH-Net) developed for face emotion recognition. At first, the input image is subjected to image denoising and contrast enhancement using the Type II Fuzzy System and cuckoo search optimization algorithm (T2FCS) filter. Then, face detection is performed using the Viola-Jones algorithm based on the resultant denoised contrast-enhanced image. Subsequently, different features, like Spider Local Image Features (SLIF) with entropy, and Local Directional Number Pattern (LDNP) are extracted from the detected face in the feature extraction phase. Finally, facial emotions at different poses are recognized from the extracted features using PNasFH-Net. Here, PNasFH-Net is developed by the integration of the Deep Pyramidal Residual Network (PyramidNet) and NASNet. The recognized classes are surprise, sad, neutral, happy, fear, disgust, contempt and anger. The benchmark dataset for facial expression recognition, AffectNet is employed to assess the performance of the proposed model using performance measures, such as accuracy, TPR, and TNR. In addition, the developed PNasFH-Net obtained a higher accuracy of 90.315 %, True Negative Rate (TNR) of 90.157 % and True Positive Rate (TPR) of 91.047 %.

关键词： Face emotion recognition Deep pyramidal residual network Neural architecture search cuckoo search optimization algorithm Local directional number pattern

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A new optimized demand management system for smart grid-based residential buildings adopting renewable and storage energies

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ENERGY REPORTS 2023年 9卷 4018-4035页

作者： Jasim, Ali M. Jasim, Basil H. Flah, Aymen Bolshev, Vadim Mihet-Popa, Lucian Univ Basrah Elect Engn Dept Basrah 61001 Iraq Iraq Univ Coll Dept Commun Engn Basrah Iraq Univ Gabes Natl Engn Sch Gabes Proc Energy Environm & Elect Syst LR18ES34 Gabes 6072 Tunisia Fed State Budgetary Sci Inst Fed Sci Agroengn Ctr Moscow 109428 Russia Ostfold Univ Coll Fac Informat Technol Engn & Econ Kobberslagerstredet 5 N-1671 Fredrikstad Norway

Demand Side Management (DSM) implies intelligently managing load appliances in a Smart Grid (SG). DSM programs help customers save money by reducing their electricity bills, minimizing the utility's peak demand, and improving load factor. To achieve these goals, this paper proposes a new load shifting-based optimal DSM model for scheduling residential users' appliances. The proposed system effectively handles the challenges raised in the literature regarding the absence of using recent, easy, and more robust optimization techniques, a comparison procedure with well-established ones, using Renewable Energy Resources (RERs), Renewable Energy Storage (RES), and adopting consumer comfort. This system uses recent algorithms called Virulence optimization algorithm (VOA) and Earth Worm optimization algorithm (EWOA) for optimally shifting the time slots of shiftable appliances. The system adopts RERs, RES, as well as utility grid energy for supplying load appliances. This system takes into account user preferences, timing factors for each appliance, and a pricing signal for relocating shiftable appliances to flatten the energy demand profile. In order to figure out how much electricity users will have to pay, a Time Of Use (TOU) dynamic pricing scheme has been used. Using MATLAB simulation environment, we have made effectiveness-based comparisons of the adopted optimization algorithms with the well-established meta-heuristics and evolutionary algorithms (Genetic algorithm (GA), cuckoo search optimization (CSO), and Binary Particle Swarm optimization (BPSO) in order to determine the most efficient one. Without adopting RES, the results indicate that VOA outperforms the other algorithms. The VOA enables 59% minimization in Peak-to-Average Ratio (PAR) of consumption energy and is more robust than other competitors. By incorporating RES, the EWOA, alongside the VOA, provides less deviation and a lower PAR. The VOA saves 76.19% of PAR, and the EWOA saves 73.8%, followed by the B

关键词： Smart Grid Demand Side Management Virulence optimization Earth Worm optimization Genetic algorithm cuckoo search optimization algorithm Binary Particle Swarm optimization

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CEPRAN-Cooperative Energy Efficient and Priority Based Reliable Routing Protocol with Network Coding for WBAN

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WIRELESS PERSONAL COMMUNICATIONS 2021年第4期117卷 3153-3171页

作者： Geetha, M. Ganesan, R. Vellore Inst Technol Sch Comp Sci & Engn Chennai Campus Chennai Tamil Nadu India

The wireless body area network (WBAN) can effectively modify the health and lifestyle monitoring specifically where multiple body parameters are measured using biomedical sensor devices. However, power consumption and reliability are crucial issues in WBAN. Cooperative Communication usually prolongs the network lifetime of WBAN and allows reliable delivery of bio-medical packets. Hence, the main aim of this investigation is to propose a novel protocol Cooperative Energy efficient and Priority based Reliable routing protocol with Network coding (CEPRAN) to enhance the reliability and energy efficiency of WBAN using cooperative communication method. Firstly, to identify a relay node from the group of sensor nodes for data forwarding, an enhanced cuckoo search optimization algorithm is proposed. Secondly, Cooperative Random Linear Network Coding approach is incorporated into the relay node to improve the packet transfer rate. CEPRAN is implemented in Ns-3 simulator and the experimental results prove that the proposed protocol outperforms the existing SIMPLE Protocol.

关键词： (WBAN cuckoo search optimization algorithm Network coding Random linear network coding)

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Fuzzy-based multiparty privacy management in social media using modified elliptic curve cryptography

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SOFT COMPUTING 2021年第8期25卷 6083-6100页

作者： Jayaprabha, P. Jacob, K. Paulose Mathew, K. Preetha Cochin Univ Coll Engn Cusat Alappuzha India Cochin Univ Sci & Technol Kochi Kerala India Cochin Univ Coll Engn Comp Sci & Engn Cusat Alappuzha India

Sharing data via social media may affect the privacy of other user's in social media. Also, multiparty privacy management is absent in social media, which leads the users incapable of managing to whom the data are shared. Because of the privacy conflicts, it is not easy to combine the privacy preferences of multiple users. For resolving the privacy conflicts in social media, more methods are required. This study promotes a fuzzy-based multiparty privacy management in social media using modified elliptic curve cryptography. The evaluation model used a method based on secure multiparty computing. Next, the fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) method is used to rank and select the participants. Finally, data encryption is performed using a modified elliptic curve encryption (MECC). Here, the optimal selection of private key is performed using the cuckoo search optimization algorithm (CSOA). With these presented techniques, the users can manage who the data are shared. In order to overcome privacy conflicts, users may first rank and select the participants based on fuzzy TOPSIS. Also, the privacy of the users is not affected by using the MECC-based data encryption framework. The presented work is implemented on the JAVA platform. The outcomes of the experiment prove that the presented approach outperforms the other existing approaches.

关键词： Secure multiparty cooperation Fuzzy TOPSIS Modified elliptic curve cryptography cuckoo search optimization algorithm Data encryption

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An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第1期10卷 267-293页

作者： Prabukumar, M. Agilandeeswari, L. Ganesan, K. VIT Univ Sch Informat Technol & Engn Vellore 632014 Tamil Nadu India VIT Univ TIFAC CORE Automot Infotron Vellore 632014 Tamil Nadu India

One of the leading causes of cancer death for both men and women is the lung cancer. The best way to improve the patient's chances for survival is the early detection of potentially cancerous cells. But, the conventional systems fails to segment the cancerous cells of various types namely, well-circumscribed, juxta-pleural, juxta-vascular and pleural-tail at its early stage (i.e., less than 3mm) that leads to less classification accuracy. It is also noted that none of the existing systems achieved accuracy more than 98%. In this paper, we propose an optimal diagnosis system not only for early detection of lung cancer nodules and also to improve the accuracy in Fog computing environment. The Fog environment is used for storage of the high volume CT scanned images to achieve high privacy, low latency and mobility support. In our approach, for the accurate segmentation of Region of Interest (ROI), the hybrid technique namely Fuzzy C-Means (FCM) and region growing segmentation algorithms are used. Then, the important features of the nodule of interest such as geometric, texture and statistical or intensity features are extracted. From the above extracted features, the optimal features used for the classification of lung cancer are identified using the cuckoosearch optimization algorithm. Finally, the SVM classifier is trained using these optimal features, which in turn helps us to classify the lung cancer as either of type benign or malignant. The accuracy of the proposed system is tested using Early Lung Cancer Action Program (ELCAP) public database CT lung images. The total sensitivity and specificity attained in our system for the above said database are 98.13 and 98.79% respectively. This results in a mean accuracy of 98.51% for training and testing in a sample of 103 nodules occurring in 50 exams. The rate of false positives per exam was 0.109. Also, a high receiver operating characteristic (ROC) of 0.9962 has been achieved.

关键词： Fog computing Fuzzy C-means Region growing segmentation cuckoo search optimization algorithm SVMclassifier

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Trajectory tracking control based on non-singular fractional derivatives for the PUMA 560 robot arm

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MULTIBODY SYSTEM DYNAMICS 2020年第3期50卷 259-303页

作者： Lavin-Delgado, J. E. Solis-Perez, J. E. Gomez-Aguilar, J. F. Escobar-Jimenez, R. F. Tecnol Nacl Mexico CENIDET Interior Internado Palmira S-N Cuernavaca 62490 Morelos Mexico Tecnol Nacl Mexico CONACyT CENIDET Interior Internado Palmira S-N Cuernavaca 62490 Morelos Mexico

In this paper, a novel hybrid fractional-order control strategy for the PUMA-560 robot manipulator is developed and presented, which combines the derivative of CaputoFabrizio and the integral of Atangana-Baleanu, both in the Caputo sense. The fractionalorder dynamic model of the system (FODM) is also considered which consists of two models, the robot manipulator model, and the model of the induction motors which are the actuators that drive their joints. The fractional model of the manipulator is obtained using the Euler-Lagrange formulation. On the other hand, for controlling each one of the induction motors, fractional-order controllers PI theta based on Atangana-Baleanu in the Caputo sense integral were developed. And for the trajectory tracking control, fractional-order controllers PD xi were developed based on the fractional derivative of Caputo-Fabrizio in the Caputo sense. Also, ordinary PI and PD controllers were developed for the PUMA robot control to compare their performance with the fractional-order controllers. The results obtained demonstrated that the fractional-order controllers have a better capability for tracking trajectory tasks than the integer-order controllers, even when changes of the desired trajectory and external disturbances are considered. Additionally, an end-effector trajectory tracking task for manufacturing applications is also considered. All numerical simulations were performed by using the same orders and gains, demonstrating that the proposed fractional-order PI theta and PD xi controllers are robust, under different operating conditions, for tracking trajectory tasks. The fractional-order controllers and the integer-order controllers were tuned applying the cuckoo search optimization algorithm where the root-mean-square error (RMSE) was chosen as the cost function to minimize.

关键词： Fractional calculus Puma 560 arm robotic manipulator Induction motor Fractional-order PI controller Fractional-order PD controller External disturbances cuckoo search optimization algorithm

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Modeling and optimization of sound absorption coefficient of microperforated compressed porous metal panel absorber

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APPLIED ACOUSTICS 2020年 166卷 107322-107322页

作者： Duan, Haiqin Shen, Xinmin Yin, Qin Yang, Fei Bai, Panfeng Zhang, Xiaonan Pan, Ming Army Engn Univ Coll Field Engn Dept Mech Engn Nanjing 210007 Jiangsu Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn State Key Lab Ultraprecis Machining Technol Kowloon Hong Kong 999077 Peoples R China

The microperforated compressed porous metal panel (MCPMP) absorber was proposed to develop novel sound absorber with excellent sound absorption performance, fewer utilized materials, and more lightweight. Through treating the compressed porous metal with high compression ratio as microperforated panel, theoretical sound absorption model of the MCPMP absorber was constructed through equivalent circuit approach. Structural parameters of the MCPMP absorber were optimized by cuckoo search algorithm for different target frequency range. The obtained optimal MCPMP absorbers were verified by finite element simulation and validated through standing wave tube measurement. Consistencies among the theoretical data, simulation data, and experimental data proved feasibility and accuracy of theoretical sound absorption model, cuckoo search optimization algorithm, and finite element simulation method. Actual average sound absorption coefficients of the optimal MCPMP absorbers with limited total thickness of 20 mm were 0.4679, 0.7069, and 0.7299 when the target frequency ranges were 100-2000 Hz, 100-4000 Hz, and 100-6000 Hz respectively. By comparison with sound absorption performance of the original porous metal and those of the 10-layer gradient compressed porous metal, effectiveness and practicality of the optimal MCPMP absorber was proved. The developed MCPMP absorber was favorable to enrich the sound absorption theory and promote its practical application. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Microperforated compressed porous metal panel absorber Theoretical sound absorption model Equivalent circuit approach cuckoo search optimization algorithm Finite element simulation Standing wave tube measurement

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T2FCS filter: Type 2 fuzzy and cuckoo search-based filter design for image restoration

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2019年第Jan.期58卷 619-641页

作者： Kumar, Sagenela Vijaya Nagaraju, C. Raj Gandhi Mem Coll Engn & Technol Nandyal India JNTUH Dept CSE Hyderabad India Yogivemana Univ YSR Engn Coll Dept CSE Proddatur India

A novel filter design for the restoration of the corrupted digital image is proposed in this paper, The filter design incorporates type II fuzzy system and cuckoo search optimization algorithm (T2FCS) based filter design for the restoration of the noise in the images. The noisy pixels in the images are detected using the proposed circular based searching scheme and the detected corrupt pixels are removed using the cuckoo search algorithm. The enhanced pixels in place of the corrupt pixels are acquired using the proposed type II fuzzy system. The proposed filter adapts to various noisy conditions such as random noise, salt and pepper noise and scratch noise. The experimentation of the proposed filter design is carried out over two images. The performance of the proposed T2FCS filter design is compared over the existing image restoration algorithms using metrics;Peak Signal to Noise ratio (PSNR), Structural Similarity Index (SSIM), Second Derivative Like Measure of Enhancement (SDME). The result obtained favours the performance of the proposed filter in the restoration of the noisy images. (C) 2018 Published by Elsevier Inc.

关键词： cuckoo search optimization algorithm Type If fuzzy system Image de-noising and restoration Second derivative like measure of enhancement

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