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A fuzzy approach for multi criteria decision making in diet plan ranking system using cuckoo optimization

NEURAL COMPUTING & APPLICATIONS 2022年第16期34卷 13625-13638页

作者： Haseena, S. Saroja, S. Revathi, T. Mepco Schlenk Engn Coll Autonomous Dept Informat Technol Sivakasi Tamil Nadu India

In recent days, people have easily adapted to unhealthy diets due to their busy lifestyles. Inappropriate and unhealthy food intake leads to various health problems. As a result of poor health and lack of information about a healthy diet, people depend on medicines rather than concentrating on improving their food intake. Due to the wide range of dietary advice, it is difficult to choose the appropriate diet plan that satisfies their personalized nutritional needs. The proposed system ranks the diet plan by considering personal information like age, gender, height, weight, pressure and heart rate. Various diet plans like intermittent fasting, plant-based diets, low-carb diets, paleo diet, low-fat diets, Mediterranean diet, DASH diet, vegan diet, gluten-free diet, GM diet and egg diet are considered. Multi-criteria decision-making methods such as fuzzy AHP and fuzzy TOPSIS are also applied for the decision-making process to rank the best diet plan. Fuzzy AHP is used for weight generation, which is given to the cuckoo algorithm for optimization, and then, the fuzzy TOPSIS method helps in ranking the diet plan. Personal information is considered as criteria, and the diet plans are considered as alternatives for the decision-making process.

关键词： Decision-making Criteria Alternatives Fuzzy AHP cuckoo optimization algorithm Fuzzy TOPSIS

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Multi-objective cuckoo optimizer for task scheduling to balance workload in cloud computing

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COMPUTING 2024年第11期106卷 3447-3478页

作者： Mondal, Brototi Choudhury, Avishek Univ Calcutta Dept Comp Sci & Engn EM.Bypass Kolkata 700094 W Bengal India New Alipore Coll Dept Comp Sci L Block Kolkata 700053 W Bengal India

A cloud load balancer should be proficient to modify it's approach to handle the various task kinds and the dynamic environment. In order to prevent situations where computing resources are excess or underutilized, an efficient task scheduling system is always necessary for optimum or efficient utilization of resources in cloud computing. Task Scheduling can be thought of as an optimization problem. As task scheduling in the cloud is an NP-Complete problem, the best solution cannot be found using gradient-based methods that look for optimal solutions to NP-Complete problems in a reasonable amount of time. Therefore, the task scheduling problem should be solved using evolutionary and meta-heuristic techniques. This study proposes a novel approach to task scheduling using the cuckoo optimization algorithm. With this approach, the load is effectively distributed among the virtual machines that are available, all the while keeping the total response time and average task processing time(PT) low. The comparative simulation results show that the proposed strategy performs better than state-of-the-art techniques such as Particle Swarm optimization, Ant Colony optimization, Genetic algorithm and Stochastic Hill Climbing.

关键词： Cloud computing Task scheduling cuckoo optimization algorithm Response time Processing time 68 49

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Performance Analysis of Meta-Heuristic algorithms for Optimal PI Tuning of PFC

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2024年第7期52卷 1039-1053页

作者： Mamizadeh, Ali Genc, Naci Electrotech Engn R&D Dept Ankara Turkiye Khoja Akhmet Yassawi Int Kazakh Turkish Univ Elect Engn Dept Turkestan Kazakhstan Yalova Univ Engn Fac Elect & Elect Engn Dept Yalova Turkiye

The popularity of the average current mode (ACM) controlled boost type power factor correction (PFC) topologies is increasing due to their suitability for power quality problems. Proportional-Integral (PI) controller method is generally used in ACM controlled boost PFC circuits which include two controllers. However, the most important complexity of ACM controlled PFC circuits is tuning the coefficients of the PI controllers optimally. Therefore, this paper proposes the optimal tuning of PI coefficients used in ACM controlled boost PFC circuits using different meta-heuristics algorithms. First, the proposed ACM controller-based boost PFC topology is analyzed in MATLAB/Simulink software by using variable loads. Then the simulation results of the cuckoo optimization algorithm (COA) based ACM controlled boost PFC converter are compared with the results determined via Ziegler-Nichols (ZN), Genetic algorithm (GA), Particle Swarm optimization (PSO), Imperialistic Competitive algorithm (ICA), and Invasive Weed optimization (IWO). Finally, the experimental verification of the topology has been done using a 600 W prototype and eZdsp F28335. As COA showed better results among other evolutionary algorithms used in this paper, we used COA parameters to observe the performance of the proposed tuning method. The experimental studies have been done under different load variations similar to the simulation studies.

关键词： power factor correction cuckoo optimization algorithm genetic algorithm meta-heuristics optimization algorithms THD

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Association rule hiding using enhanced elephant herding optimization algorithm

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AUTOMATIKA 2024年第1期65卷 98-107页

作者： Rajasekaran, M. Thanabal, M. S. Meenakshi, A. Kamaraj Coll Engn & Technol Dept Comp Sci & Engn Virudunagar Tamil Nadu India PSNA Coll Engn & Technol Dept Comp Sci & Engn Dindigul India

Association rule hiding is an efficient solution that helps organizations to avoid the risk caused by sensitive knowledge leakage when sharing data in their collaborations. cuckoo optimization algorithm (COA) sanitizes the transaction database but this method has limitation due to its slow convergence and exploitation capabilities. Hence in this paper, Enhanced Elephant Herding optimization algorithm for Association Rule Hiding (EEHOA4ARH) is proposed for association rule hiding. In EEHOA, two core functions such as clan updating operator and separating operator are used for association rule hiding that also realizes the fast convergence and exploitation capabilities. Moreover, the searching strategy in COA4ARH for the selection of best solution is highly time consuming. To reduce the time consumption for the selection of best solution, a Crowding Distance (CD) concept is combined with EEHOA4ARH. By continuously updating the best elephant and replacing the worst elephant in the population, EEHOA4ARH-CD sanitizes the transaction database effectively. Thus the proposed EEHOA4ARH achieves the less computation time, fast convergence and better exploitation capabilities by using crowding distance. The experimental results prove the effectiveness of the proposed EEHOA4ARH-CD method in terms of hiding failure, lost rule and execution time with 44.66 s.

关键词： Association rule hiding evolutionary algorithm cuckoo optimization algorithm enhanced elephant optimization algorithm crowding distance

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Estimation of load capacity of driven piles by COA-MLP and COA-SVR models: a comparative study

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 1553-1564页

作者： Song, Yuhua Chongqing Vocat Coll Appl Technol Sch Intelligent Construct & Management Chongqing 401520 Peoples R China

Numerous theoretical and empirical models have been presented for calculating the load capacity in deep foundations in geotechnical engineering. In this case, the models are driven mostly by physical assumptions and the building of estimates using mathematical frameworks. In the present essay, two hybrid support vector regression (SVR) and multilayer perceptron neural network (MLP) models optimized with cuckoo optimization algorithm (COA) have been used to calculate the experimental dataset's load capacity estimate for driven piles (Q(t)). The uniqueness of this study stems from several factors, including the utilization of the COA, the assessment of Q(t), the comparison with previous studies, and the incorporation of a substantial dataset with multiple input variables. Together, these elements present a fresh approach to enhance the accuracy of prediction models and further our knowledge in forecasting the Q(t). Regarding models' effectiveness indices, it was obvious that both SVR and MLP optimized with COA perform wonderfully performance in the train and test categories, with a maximum R-2 of 0.9748 and 0.9801, respectively, which depicts the great correlation between measured and predicted Q(t). The SVR model optimized using COA performs better than the COA-MLP one when MAE and RMSE are considered. At last, the assigned ranking system depicts that the COA-SVR model outperforms COA-MLP network by gaining the highest scores for all indices with a total ranking score of 16. Ultimately, it is apparent that the SVR combined with COA could score higher than the MLP, which is the proposed system in Q(t) forecasting model after assessing the reliability and considering the assumptions.

关键词： Deep foundation Driven piles Load-bearing capacity Prediction cuckoo optimization algorithm MLP SVR

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SPWM Sampling Method Based on Area Difference optimization

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第2期18卷 909-924页

作者： You, Hong Hu, Haize Fang, Mengge Hunan Inst Engn Sch Elect & Informat Engn Xiangtan Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan Peoples R China State Grid Yiyang Power Supply Co Yiyang Peoples R China

To solve the area solving error problem of the rule sampling method in the SPWM pulse calculation process, the paper propose an optimized rule sampling method to correct the area difference. The method mainly use infinitesimal components to continuously correct the area difference, and then reduce the error components. To find the best infinitesimal component value, the cuckoo optimization algorithm was used to iteratively calculate the infinitesimal component. To study the effectiveness of the proposed sampling method, MATLAB software was used to construct a sampling model with the natural sampling method as the benchmark, the relative error and the average error as the evaluation index, and the symmetric regular sampling method, the asymmetric sampling method and the tangent sampling method as the comparison method. The experimental results showed that compared with the three comparison methods, the proposed method reduce the absolute value of relative error at the turn-on time by 39%, 0.4% and 26.5%, and the absolute value of relative error at the turn-off time by 63.7%, 0.1% and 20.2%, which is closer to the natural sampling method. The proposed method can effectively reduce the sampling error in the pulse width modulation process and improve the inversion accuracy.

关键词： SPWM Regular sampling Area difference Infinitesimal component cuckoo optimization algorithm

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Stability enhancement of hybrid electric vehicles using optimal fuzzy logic

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2023年第4期12卷 1130页

作者： Muhammad, Ebrahim Vali, Ahmadreza Kashaninia, Abdoreza Bahnamgol, Vahid Malek Ashter Univ Technol Dept Elect & Comp Engn Tehran Iran

This paper investigates a fuzzy control method based on the cuckoo optimization algorithm (COA) strategy to enhance the lateral dynamics of the vehicle under uncertain vehicle model parameters. The direct yaw control system has a two-layer control structure. A corrective yaw moment (M-z) is obtained in the upper layer by optimizing the fuzzy controller's input-output weights and membership functions based on COA. In the lower layer, the corrective yaw moment is converted into braking torque, which is applied to the left or right rear wheels depending on the distribution algorithm. Simulations of three scenarios under various road conditions were conducted to ensure the robustness of the proposed control strategy against changes in road conditions. A nonlinear 8-DoF dynamic model for the vehicle with a Dugoff tire model is used for computer simulations on MATLAB. Simulation results during a single lane change and J-turn maneuvers on a dry and rainy road compared the performance of the suggested optimal fuzzy controller with the non-optimal fuzzy controller. The simulation results showed that the proposed control system improved the stability and handling of the system's tracking performance in various maneuvers.

关键词： Vehicle lateral stability Direct yaw control Fuzzy control cuckoo optimization algorithm

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Efficient operation of combined residential and commercial energy hubs incorporating load management and two-point approximation for uncertainty modeling

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 116卷

作者： Zhao, Renlu Zheng, Weiyong Lyceum Philippines Univ Claro M Recto Acad Adv Studies Manila 1002 Philippines Huaihe Energy Huainan Panji Power Generat Co Ltd Equipment Management Dept Huainan 232001 Peoples R China ShangHai Univ Int Business & Econ Sch Foreign Languages Shanghai 201620 Peoples R China

In the era of advancing technology, energy networks have evolved into sophisticated smart networks, allowing seamless communication and exchange of information among network components. This transformation opens doors for innovative concepts such as the Multi -Carrier Energy System, or Energy Hub. While integrating energy hubs into traditional networks has posed challenges, the expansion of smart networks has facilitated their adoption within the power grid. Unlike conventional systems that optimize a single energy carrier, energy hubs focus on the optimal operation of a system encompassing multiple energy carriers like natural gas, electric grid, and thermal energy. Serving as intermediaries between energy supplier companies and diverse end -users, these hubs play a pivotal role in the intricate landscape of modern energy systems. This paper introduces a novel strategy for optimizing the utilization of residential and commercial energy hubs, specifically integrating Time -of -Use (ToU) tariffs and Load Response Programs (LRPs). To address inherent uncertainties, the proposed system employs the two -point estimation method within its model. For achieving optimal scheduling of energy resources aligned with ToU tariffs and load management plan signals, the well -established cuckoo optimization algorithm (COA) is applied. The results presented in this study highlight the effectiveness of the proposed approach in significantly reducing energy costs for the energy hub. Notably, these cost reductions are achieved while adhering to the constraints of the ToU Load Management Program (LMP). Furthermore, the paper explores various scenarios to investigate the impact of LRPs on both residential and commercial energy hubs, providing valuable insights into the dynamics of these systems. The numerical outcomes of the study affirm the efficacy of the proposed energy hub system. Implementation of load management plans markedly reduces unsupplied electrical and thermal energy, with

关键词： Residential and commercial energy hub integration Energy system operation Two-point estimation Load management program cuckoo optimization algorithm

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Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2024年 41卷

作者： Davoodi, Shadfar Thanh, Hung Vo Wood, David A. Mehrad, Mohammad Hajsaeedi, Mohammad Reza Rukavishnikov, Valeriy S. Tomsk Polytech Univ Sch Earth Sci & Engn Lenin Ave Tomsk Russia Van Lang Univ Inst Computat Sci & Artificial Intelligence Lab Computat Mech Ho Chi Minh City Vietnam Van Lang Univ Fac Mech Elect & Comp Engn Sch Technol Ho Chi Minh City Vietnam DWA Energy Ltd Lincoln England Shahrood Univ Technol Sch Min Petr & Geophys Engn Shahrood Iran

This study explores the development of predictive models for carbon dioxide (CO2) solubility in ionic liquids based on a compiled dataset of 10,116 experimentally measured data points involving four input variables: pressure (P), temperature (T), cation type, and anion type. The deep-learning (DL) predictive models evaluated are standalone and hybrid versions of convolutional neural network (CNN) and long short-term memory (LSTM) algorithms with cuckoo optimization algorithm (COA) and gradient-based optimization (GBO). The laboratory- measured data was separated into training and test categories, and each category was normalized separately to improve the performance of the deep learning algorithms. The Mahalanobis distance-based quantile method was utilized to identify any outliers in the training data. Once identified, the outlier data points were eliminated from the training dataset. The control parameters of the deep learning algorithms were optimized using COA to enhance their efficiency, and the algorithms were hybridized with optimization algorithms to further improve their performance. The resulting models were analyzed to assess their accuracy, degree of overfitting, and the importance of input features. The study found that using 80% of the data for training and 20% for testing results in more accurate and generalizable models. Using the outlier detection method on the training data led to 307 data points being eliminated as outliers. Developing CO2-solubility predictive model showed that, the CNN-COA model had the lowest RMSE and highest R2 among the developed models, indicating high generalizability for data unseen by the trained model. The analysis revealed that using optimization algorithms increased the CO2-solubility prediction performance of DL algorithms and reduced overfitting. T and cation type were the most and least important input features, respectively. Simultaneous changes in cation and anion type on CO2-solubility predictions displayed no sy

关键词： Ionic water Hybrid deep-learning Convolutional neural network cuckoo optimization algorithm CO2 solubility

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Event-based neural network predictive controller application for a distillation column

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ASIAN JOURNAL OF CONTROL 2021年第2期23卷 811-823页

作者： Hadian, Mohsen Mehrshadian, Milad Karami, Mahboobeh Biglary Makvand, Amin Petr Univ Technol Dept Instrumentat & Automat Ahwaz Iran Univ Calgary Dept Elect & Comp Engn Calgary AB Canada

In this work, the case study is a distillation column, which is a multi-input multi-output (MIMO) nonlinear process. An event-based neural network predictive controller is utilized for the case study, considering control and energy policies. Computation and communication reduction are the main purposes of the event-based strategy. The event-based model predictive controller also copes successfully with the multi-input multi-output (MIMO) time-delayed nonlinear processes. In order to achieve a suitable nonlinear data-based model of the process, an event-based neural network predictive controller is proposed. Moreover, new cuckoo optimization algorithm (COA) is employed to improve the efficiency of the neural network. Evaluation of the proposed controller has illustrated a satisfactory performance in both setpoint tracking and disturbance rejection while computational load has been significantly decreased.

关键词： event-based controller model predictive controller neural network cuckoo optimization algorithm distillation column

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