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Development and Design of an Optimal Fuzzy Logic Two Degrees of Freedom-Proportional Integral Derivative Controller for a Two-Area Power System Using the bee algorithm

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ENERGIES 2025年第4期18卷 915-915页

作者： Audomsi, Sitthisak Wattana, Supannika Uthathip, Narongkorn Sa-ngiamvibool, Worawat Mahasarakham Univ Fac Engn Maha Sarakham 44150 Thailand Mahasarakham Univ Elect & Comp Engn Res Unit Maha Sarakham 44150 Thailand

This study introduces a fuzzy logic-based two-degree-of-freedom PID (FL2DOF-PID) controller that is optimized using the bee algorithm (BA) to control the load frequency in a two-area linked power system that has both reheat thermal power plants and hydro power plants. To test how well it works, MATLAB/Simulink simulations compared it with PID, 2DOF-PID and fuzzy PID controllers, looking at overshoot, undershoot, settling time, steady-state error and the integral of absolute error (IAE). The results showed that FL2DOF-PID had the lowest RMSE (0.0054, 0.0089) and MAE (0.0041, 0.0065), as well as the smallest IAE (0.1308) and the smallest overshoot (69.3% less). It also had the fastest settling time (5.1528 s) and the smallest IAE (0.1338 less). These results showed that it works to reduce frequency changes, improve power flow stability and make the whole system more reliable under changing conditions.

关键词： Automatic Generation Control 2DOF-PID controller fuzzy logic 2DOF-PID controller PID controller bee algorithm

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bee algorithm and Adaptive Neuro-Fuzzy Inference System as Tools for QSAR Study Toxicity of Substituted Benzenes to Tetrahymena pyriformis

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BULLETIN OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2014年第6期92卷 642-649页

作者： Zarei, Kobra Atabati, Morteza Kor, Kamalodin Damghan Univ Sch Chem Damghan Iran

A quantitative structure-activity relationship (QSAR) was developed to predict the toxicity of substituted benzenes to Tetrahymena pyriformis. A set of 1,497 zero- to three-dimensional descriptors were used for each molecule in the data set. A major problem of QSAR is the high dimensionality of the descriptor space;therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm was used to select the best descriptors. Three descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Then the model was corrected for unstable compounds (the compounds that can be ionized in the aqueous solutions or can easily metabolize under some conditions). Finally squared correlation coefficients were obtained as 0.8769, 0.8649 and 0.8301 for training, test and validation sets, respectively. The results showed bee-ANFIS can be used as a powerful model for prediction of toxicity of substituted benzenes to T. pyriformis.

关键词： Toxicity Substituted benzenes QSAR ANFIS bee algorithm

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Using bee algorithm for peer-to-peer file searching in mobile ad hoc networks

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2011年第5期34卷 1498-1508页

作者： Dhurandher, Sanjay K. Misra, Sudip Pruthi, Puneet Singhal, Shubham Aggarwal, Saurabh Woungang, Isaac Ryerson Univ Dept Comp Sci Toronto ON Canada Univ Delhi Div Informat Technol Netaji Subhas Inst Technol New Delhi India Indian Inst Technol Sch Informat Technol Kharagpur 721302 W Bengal India Univ Delhi Div Comp Engn Netaji Subhas Inst Technol New Delhi India

In this work, we present a P2PBA algorithm that refers to a peer-to-peer file searching method, which uses the bees algorithm. The proposed algorithm has been designed with an aim towards providing an efficient peer-to-peer (P2P) file search in mobile ad hoc networks (MANETs). With reference to Li et al. (2004) and Gerla and Lindemann (2005) it has been observed that the implementation of P2P file sharing system on MANETs is quite tricky to implement as compared to that on a wired network. With the advent of swarm intelligence, the P2P file sharing methodology not only found an optimized search process involving a more selective node tracing, but it also proved to improve the time efficiency and robustness of the sharing mechanism. The P2P file searching system implementation, particularly in a network of mobile nodes, poses: (a) the percentage network area scanned and (b) the selective file retrieval from a set of file bearing nodes as the biggest challenges. Managing nodes scattered over a large terrain is not easy. Node connectivity and file information become more volatile as the network area increases. Probability of retrieving a file from a profitable source is also a yardstick to determine how good the file retrieval algorithm is. This algorithm referred to as P2PBA implements the P2P file searching process using the bee algorithm (Pham et al., 2006b;Wedde and Farooq, 2005) and aims at solving these two challenges. This scheme of swarm-based intelligence, which is based on the foraging behavior of honey bees, optimizes the search process selectively hunting for more promising honey sources and scans a sizeable area in a more comprehensive manner. Following a description of the proposed algorithm, this paper finally presents the simulation results for the network against various specified parameters. The simulation results show that our algorithm proposes to make file searching much more efficient and improves the statistics against the posed challenges. (C) 2

关键词： bee algorithm Peer-to-peer file search Mobile ad hoc networks Swarm based intelligence Simulations

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A hybrid AI-based particle bee algorithm for facility layout optimization

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ENGINEERING WITH COMPUTERS 2012年第1期28卷 57-69页

作者： Cheng, Min-Yuan Lien, Li-Chuan Natl Taiwan Univ Sci & Technol Dept Construct Engn Taipei 106 Taiwan

Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult combinatorial optimization problem for engineers. Swarm intelligence, an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm-the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows technique for improving searching efficiency as well as a self-parameter-updating technique for preventing trapping into a local optimum in high-dimensional problems. This study compares PBA performance against BA and PSO performance in practical FL problem. Results show PBA performance is comparable to those of BA and PSO and can be efficiently employed to solve practical FL problem with high dimensionality.

关键词： Facility layout Swarm intelligence bee algorithm Particle swarm optimization Particle bee algorithm

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Estimating Seebeck Coefficient of a p-Type High Temperature Thermoelectric Material Using bee algorithm Multi-layer Perception

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JOURNAL OF ELECTRONIC MATERIALS 2017年第8期46卷 4931-4938页

作者： Uysal, Fatih Kilinc, Enes Kurt, Huseyin Celik, Erdal Dugenci, Muharrem Sagiroglu, Selami Karabuk Univ Dept Mech Engn Fac Engn Karabuk Turkey Dokuz Eylul Univ Ctr Prod & Applicat Elect Mat EMUM Izmir Turkey Dokuz Eylul Univ Dept Met & Mat Engn Fac Engn Izmir Turkey Dokuz Eylul Univ Grad Sch Nat & Appl Sci Dept Nanosci & Nanoengn Izmir Turkey Karabuk Univ Dept Ind Engn Fac Engn Karabuk Turkey

Thermoelectric generators (TEGs) convert heat into electrical energy. These energy-conversion systems do not involve any moving parts and are made of thermoelectric (TE) elements connected electrically in a series and thermally in parallel;however, they are currently not suitable for use in regular operations due to their low efficiency levels. In order to produce high-efficiency TEGs, there is a need for highly heat-resistant thermoelectric materials (TEMs) with an improved figure of merit (ZT). Production and test methods used for TEMs today are highly expensive. This study attempts to estimate the Seebeck coefficient of TEMs by using the values of existing materials in the literature. The estimation is made within an artificial neural network (ANN) based on the amount of doping and production methods. Results of the estimations show that the Seebeck coefficient can approximate the real values with an average accuracy of 94.4%. In addition, ANN has detected that any change in production methods is followed by a change in the Seebeck coefficient.

关键词： Thermoelectric material thermoelectric generator Seebeck coefficient artificial neural network back propagation bee algorithm

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Optimization of Wireless Sensor Network Coverage using the bee algorithm

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2020年第2期36卷 377-386页

作者： Khalaf, Osamah Ibrahim Abdulsahib, Ghaida Muttashar Sabbar, Bayan Mahdi Al Nahrain Univ Coll Informat Engn Baghdad 10072 Iraq Univ Technol Baghdad Dept Comp Engn Baghdad 10066 Iraq

In this paper, a method for optimizing the wireless sensor network coverage was introduced using the bee algorithm. We simulated the proposed method in MATLAB software and compared the obtained results with the genetic algorithm. The results showed that the bee algorithm gives more optimal coverage percentage compared to the genetic algorithm and uses less time to use the system resources and implement the algorithm.

关键词： bee algorithm wireless sensor network WSN coverage sensors optimization

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A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第10期39卷 9642-9650页

作者： Lien, Li-Chuan Cheng, Min-Yuan Natl Taiwan Univ Sci & Technol Dept Construct Engn Taipei 106 Taiwan

The construction site layout (CSL) design presents a particularly interesting area of study because of its relatively high level of attention to usability qualities, in addition to common engineering objectives such as cost and performance. However, it is difficult combinatorial optimization problem for engineers. Swarm intelligence (SI) was very popular and widely used in many complex optimization problems which was collective behavior of social systems such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This study proposed an optimization hybrid swarm algorithm namely particle-bee algorithm (PBA) based on a particular intelligent behavior of honey bee and bird swarms by integrates theirs advantages. This study compares the performance of PBA with that of BA and PSO for hypothetical construction engineering of CSL problems. The results show that the performance of PBA is comparable to those of the mentioned algorithms and can be efficiently employed to solve those hypothetical CSL problems with high dimensionality. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.

关键词： Construction site layout Swarm intelligence bee algorithm Particle swarm optimization Particle-bee algorithm

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Shuffling cross-validation-bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography

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JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH PART B-PESTICIDES FOOD CONTAMINANTS AND AGRICULTURAL WASTES 2017年第5期52卷 346-352页

作者： Zarei, Kobra Atabati, Morteza Ahmadi, Monire Damghan Univ Sch Chem Damghan Iran

bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross-validation-BA (CV-BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV-BA. The results showed that shuffling CV-BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV-BA-MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV-BA-SVM method were obtained as 0.0704 and 0.9922, respectively.

关键词： Pesticides bee algorithm shuffling cross-validation quantitative structure property relationship (QSPR) variable selection method

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Predicting the heats of combustion of polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds using bee algorithm and adaptive neuro-fuzzy inference system

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2013年 128卷 37-48页

作者： Zarei, K. Atabati, M. Moghaddary, S. Damghan Univ Sch Chem Damghan Iran

A new method was developed for prediction of the heats of combustion of important classes of energetic compounds including polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds. A set of 1497 zero- to three-dimensional descriptors was generated for each molecule in the data set. A major problem of modeling is the high dimensionality of the descriptor space;therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm (BA) was used to select the best descriptors. bee algorithm is a new population-based optimization algorithm, which is derived from the observation of real bees and proposed to feature selection. Four descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Squared correlations of coefficients were obtained as 0.9980, 0.9996 and 0.9988 for training, test and validation sets, respectively. In comparison with genetic algorithm (GA)-ANFIS and multiple linear regression (MLR)-ANFIS, the results showed that bee-ANFIS can be used as a powerful model for prediction of heats of combustion of these compounds. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Heats of combustion Energetic nitrogen containing compounds ANFIS bee algorithm

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Classification model for heart disease prediction with feature selection through modified bee algorithm

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SOFT COMPUTING 2022年第23期26卷 13049-13057页

作者： Velswamy, Karunakaran Velswamy, Rajasekar Swamidason, Iwin Thanakumar Joseph Chinnaiyan, Selvan Karunya Inst Technol & Sci Coimbatore Tamil Nadu India SRM Inst Sci & Technol Vadapalani Campus Chennai Tamil Nadu India Natl Inst Technol Trichy Tamil Nadu India

Nowadays, a healthcare field produces a huge amount of data;for processing those data, some efficient techniques are required. In this paper, a classification model is developed for heart disease prediction and the attribute selection is carried out through a modified bee algorithm. The prediction of heart disease through models will help the practitioners to make a precise decision about patient health. Heart disease dataset is obtained from the UCI repository. Dataset consists of 76 features and all those seventy-six features have not contributed equal information during the time of classification. In the entire attributes, some of the attributes will contribute a large amount of information at the time of classification task and some of the attributes will contribute only a small amount of information during the classification task. In this paper, a modified bee algorithm is used to identify the best subset of attributes from the entire features in the dataset;i.e., the training phase of classification only retains those features that are contributing more information during classification task and it will reduce the training time of classifiers. The experiment is analyzed with an obtained reduced subset of attributes by using the following classifiers such as support vector machine, Naive Bayes and KNN. The experimental result shows that the support vector machine classifier will provide a good classification accuracy, true positive rate, true negative rate, false positive rate and false negative rate compared to Naive Bayes and KNN.

关键词： Classification Feature selection bee algorithm Support vector machine Naive Bayes and KNN

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