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Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2019年第4期35卷 1231-1244页

作者： Zhao, Bin Ren, Yi Gao, Diankui Xu, Lizhi Zhang, Yuanyuan Liaoning Shihua Univ Sch Mech Engn Fushun Peoples R China

The proper maintenance plan should be made for ensuring the safety and reliability of polypropylene plant and improve economic benefits of petrochemical enterprise. To meet the requirement, a novel maintenance prediction model of polypropylene plant based on fuzzy theory, ridgelet an artificial neural network is constructed. The economy and reliability models of polypropylene plant maintenance are established through comprehensively considering the reliability and economy. The basic structure of fuzzy ridgelet neural network is designed, and the training algorithm is improved through combining the traditional particle swarm algorithm and bacterial foraging algorithm, and the corresponding algorithm flow is confirmed. Finally, prediction simulation analysis is carried out using a polypropylene plant as research object, and analysis results show that the fuzzy ridgelet neural network has best prediction effect, and the optimal maintenance plan can be confirmed to ensure security and reduce maintenance cost of polypropylene plant.

关键词： bacterial foraging algorithm fuzzy ridgelet neural network maintenance cost polypropylene plant reliability

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Image segmentation using fuzzy competitive learning based counter propagation network

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第24期78卷 35263-35287页

作者： Chouhan, Siddharth Singh Kaul, Ajay Singh, Uday Pratap Shri Mata Vaishno Devi Univ Sch Comp Sci & Engn Katra 182320 India Shri Mata Vaishno Devi Univ Sch Math Katra 182320 India

Image segmentation is the method of partitioning an image into some homogenous regions that are more meaningful for its better understanding and examination. Soft computing methods having the capabilities of achieving artificial intelligence are predominately used to perform the task of segmentation. Due to the variability and the uncertainty present in natural scenes, segmentation is a complicated task to perform with the help of conventional image segmentation techniques. Therefore, in this article a hybrid Fuzzy Competitive Learning based Counter Propagation Network (FCPN) is proposed for the segmentation of natural scene images. This method compromises of the uncertainty handling capabilities of the fuzzy system and proficiency of parallel learning ability of neural network. To identify the number of clusters automatically in less computational time, the instar layer of Counter propagation network (CPN) has been trained by using Fuzzy competitive learning (FCL). The outstar layer of counter propagation network is trained by using Grossberg learning for obtaining the desired output. Region growing method having the tendency to correctly identify edges with simplicity is used for initial seed point selection. Then, the most similar regions in the image are clustered and the number of clusters is estimated automatically. Finally, by identifying the cluster centers the images are segmented. bacterial foraging algorithm is used to initialize the initial weights to the network, which helps the proposed method in achieving low convergence ratio with higher accuracy. Results validated the higher performance of proposed FCPN method when compared with other states-of-the-art methods. For future work, some other adaptive methods like the fuzzy model-based network can be used to identify multiple object regions and classifying them among separate clusters.

关键词： bacterial foraging algorithm Counter propagation network Fuzzy competitive learning Image segmentation Soft computing

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A new multi-objective for environmental and economic management of Volt/Var Control considering renewable energy resources

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ENERGY 2013年 55卷 236-252页

作者： Zare, Mohsen Niknam, Taher Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

Environmental and economical aspects of Volt/Var Control problem as well as technical issues of the networks, transform the classical problem into multiobjective one. In this paper, a Multiobjective theta-Smart bacterial foraging algorithm (M theta-SBFA) is proposed to solve the WC problem in distribution networks including Renewable Energy Sources (RESs) involving the conflicting objectives;i.e. the electrical energy losses, the voltages deviations, the total electrical energy costs and the total emissions of RESs and substations. The proposed algorithm goes through the search space in the polar coordinates instead of the Cartesian one;whereby the feasible space is more compact. Also, several modifications are applied to restraint the premature convergence of the solutions. Hence, the bacteria in chemotaxis process are moved in short or long steps as well as swimming movement. Furthermore, replacement the least healthy bacteria are done by the strong dominated solutions reserved along the iterative search process. A new approach based on the variance of the objective functions is utilized to select the population;thereby lead to uniformly distributed the Pareto Optimal Front (POF) as well as reach to extreme points of the solutions. Niching mechanism, besides the smart population, leads the bacteria towards the lesser covered space of the POF. A fuzzy clustering technique controls the size of the repository when it gets filled. Finally, two test distribution feeders are used to assess the performance of the proposed modifications. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： bacterial foraging algorithm Pareto dominance Volt/Var control theta-search space

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Artificial neural network integrated with bio-inspired approach for optimal VAr management and voltage profile enhancement in grid system

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2021年第21期43卷 2838-2859页

作者： Chandrasekaran, Kumar Selvaraj, Jaisiva Xavier, Felix Joseph Kandasamy, Prabaakaran M Kumarasamy Coll Engn Dept EEE Karur Tamil Nadu India IFET Coll Engn Dept EEE Villupuram Tamil Nadu India Bule Hora Univ Elect & Comp Engn Dept Hagere Maryam Ethiopia Vel Tech High Tech Dr Rangarajan Dr Sakunthala En Dept EEE Chennai Tamil Nadu India

This article presents the design of an improved smart grid model with an improved optimal VAr strategy using a bio-inspired approach. The integration of an artificial neural network technique with bio-inspired algorithms is the novelty of this research work. The objective of the work is attained by integrating the artificial neural network with three different evolving techniques like the Big Bang-Big Crunch algorithm, the gravitational search algorithm, and bacterial foraging algorithm. The three different layers of artificial neural network reconcile with the power grid for enhancing the voltage profile generated by bio-inspired techniques. The effectiveness and reliability of the algorithms are examined on the standard IEEE 30 bus system. The sample test bus system comprises six Generators, four Transformer tap changers, and 2 Static Shunt VAr Compensator. Artificial intelligence technology is applied for enhancing the voltage profile by managing reactive power across the power system network. The results produced by artificial intelligence technique are more competent and outstanding than any other conservative optimization techniques. It is found that the real power loss across the network has been reduced from 5.744 MW to 4.234 MW, while the bus voltage deviation has been minimized from 1.475 p.u. to 0.0918 p.u. and the voltage profile has been improved from 0.968 to 1.0985 p.u. after integration of the artificial neural network. The optimal values of objective functions are attained by evaluating the training and testing performance of artificial neural network criteria such as coefficient of determination, mean absolute error, root mean square error, and mean absolute percentage error. The obtained results show the remarkable improvement in voltage profile after integrating artificial intelligence technology with a power grid system.

关键词： Artificial Neural Network Gravitational Search algorithm Big Bang-Big Crunch algorithm bacterial foraging algorithm Active Power Loss Voltage Deviation Voltage Profile

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Enhancing the Performance of LSSVM Model in Predicting Rock Fragmentation Size Via Optimization algorithms

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KSCE JOURNAL OF CIVIL ENGINEERING 2023年第9期27卷 3765-3777页

作者： Zheng, Haowen Liu, Jun Zhuang, Ruihong Zhao, Futian Zhen, Mengyang Wang, Yue Liu, Zheng Hohai Univ Coll Civil & Transportat Engn Nanjing 210098 Peoples R China

The prediction of rock fragmentation is critical to improve the efficiency and economy of blasting excavation. In this study, an attempt is made to predict the entire fragmentation size distribution using a least square support vector machine (LSSVM) model. In addition, three optimization algorithms-the bacterial foraging algorithm (BFO), artificial fish swarm algorithm (AFSA), and adaptive particle swarm optimization (APSO)-were used to determine the appropriate parameters of the LSSVM model. In the constructed LSSVM-BFO, LSSVM-AFSA and LSSVM-APSO models, the hole spacing, row spacing, change per delay and stemming were used as the input parameters, while the statistical rock fragmentation size was assigned as the output. The LSSVM model was also employed as a control group for comparing with the optimized models. The above-mentioned models were trained and tested based on a database comprising of 10 datasets collected from in-site testing of Altashi Water Control Project in China. The performance of the proposed models was compared by several statistical criteria. The viability and efficiency of the LSSVM-BFO model were confirmed with an R-2 of 0.9960 and an RMSE of 1.8044, which were better than those of the LSSVM-AFSA, LSSVM-APSO and LSSVM. Last but not least, sensitivity analysis was also executed. The result of sensitivity analysis demonstrated when the size of rock fragmentation of prediction is less 80mm, the most effective parameter will be stemming;otherwise the most effective parameter will be hole spacing.

关键词： Rock fragmentation LSSVM bacterial foraging algorithm Artificial fish swarm algorithm Adaptive particle swarm optimization

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Optimizing human reliability in work shifts scheduling: A real case of clothing job shop

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2023年第8期39卷 3518-3536页

作者： Arekamari, Mohammad Akbari Namaghi, Mohammad Ghasemi Payame Noor Univ Fac Management & Econ Tehran Iran

In this research, we provide a mathematical model with an objective function of minimizing human errors for the problem of shift scheduling optimization. The unique aspects which are considered in our model are learning, forgetting, fatigue, and rest parameters (which affect the human reliability). Various problems with different human parameters, based on real cases of clothing job shops, are solved to study the efficiency and effectiveness of the optimization model. Two improved bacterial foraging algorithms are presented to handle the complexity of our problem. To examine the capability of the presented swarm intelligence algorithms in obtaining global optimum problem, we compare the results of the algorithms against the solution of basic bacterial foraging algorithm in terms of solutions quality. Results indicate that the solution approaches are more efficient in searching solution space and convergence. Analysis of different shift scheduling shows that in moderate fatigue condition, the scenario of scheduling with continuous work hours is better in terms of productivity. Also, experiments demonstrate that optimum reliable shift schedules are related to human factors. The findings of our research indicate that the presented scheduling model and improved algorithms can provide optimum shift schedules regarding human factors.

关键词： bacterial foraging algorithm clothing job shop human parameters human reliability scheduling scenarios

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Improving power quality with reducing output harmonics for electric vehicle applications by using bacteria foraging algorithm

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INTERNATIONAL JOURNAL OF ELECTRIC AND HYBRID VEHICLES 2016年第1期8卷 41-60页

作者： Farsadi, Murtaza Ahmadi, Monireh Urmia Univ Dept Elect Engn Orumiyeh *** Iran Islamic Azad Univ Urmia Branch Dept Elect Engn Orumiyeh *** Iran

The requirements for the power inverter of the electric vehicles ( EVs) include high peak power, low output harmonics, inexpensive circuit and low continuous power rating. The purpose of this paper is to reduce the output harmonics of a cascade H-bridge multilevel inverter for EV applications by means of a bacterial foraging algorithm ( BFA). In this study, the modified BFA is proposed as a technique for solving non-linear equations of selective harmonics elimination technique for determining optimal switching angles in multilevel inverters. The proposed optimisation method enhances the convergence rate and accuracy in dealing with complex problems with high dimensions, by adding improved and dynamic aspects to its main algorithm. Also, in this paper, a lower number of DC voltage sources have been used in the provided inverter compared with common multilevel inverters;this has resulted in the reduction in price of the inverter. Simulation results on various conditions were carried out using PSCAD/EMTDC software.

关键词： cascaded H-bridge multilevel inverter power quality modulation method switching angles harmonics BFA bacterial foraging algorithm

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Multiple order-up-to policy for mitigating bullwhip effect in supply chain network

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ANNALS OF OPERATIONS RESEARCH 2018年第1-2期269卷 361-386页

作者： Keshari, Anupam Mishra, Nishikant Shukla, Nagesh McGuire, Steve Khorana, Sangeeta MNNIT Dept Mech Engn Allahabad Uttar Pradesh India Univ Hull Hull Univ Business Sch Kingston Upon Hull N Humberside England Univ Wollongong Fac Engn & Informat Sci SMART Infrastruct Facil Wollongong NSW Australia Univ Sussex Sch Business Management & Econ Brighton Sussex England Bournemouth Univ Business Sch Bournemouth Dorset England

This paper proposes a multiple order-up-to policy based inventory replenishment scheme to mitigate the bullwhip effect in a multi-stage supply chain scenario, where various transportation modes are available between the supply chain (SC) participants. The proposed policy is similar to the fixed order-up-to policy approach where replenishment decision "how much to order" is made periodically on the basis of the pre-decided order-up-to inventory level. In the proposed policy, optimal multiple order-up-to levels are assigned to each SC participants, which provides decision making reference point for deciding the transportation related order quantity. Subsequently, a mathematical model is established to define optimal multiple order-up-to levels for each SC participants that aims to maximize overall profit from the SC network. In parallel, the model ensures the control over supply chain pipeline inventory, high satisfaction of customer demand and enables timely utilization of available transportation modes. Findings from the various numerical datasets including stochastic customer demand and lead times validate that-the proposed optimal multiple order-up-to policy based inventory replenishment scheme can be a viable alternative for mitigating the bullwhip effect and well-coordinated SC. Moreover, determining the multiple order-up-to levels is a NP hard combinatorial optimization problem. It is found that the implementation of new emerging optimization algorithm named bacterial foraging algorithm (BFA) has presented superior optimization performances. The robustness and applicability of the BFA algorithm are further validated statistically by employing the percentage heuristic gap and two-way ANOVA analysis.

关键词： Bullwhip effect Optimal order-up-to inventory level bacterial foraging algorithm Supply chain

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Optimal Intelligence Planning of Wind Power Plants and Power System Storage Devices in Power Station Unit Commitment Based

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Energy Engineering 2022年第5期119卷 2081-2104页

作者： Yuchen Hao Dawei Su Zhen Lei Control Center of State Grid of Jiangsu Electric Power Co. Ltd.Nanjing210000China

Renewable energy sources(RES)such as wind turbines(WT)and solar cells have attracted the attention of power system operators and users alike,thanks to their lack of environmental pollution,independence of fossil fuels,and meager marginal *** the introduction of RES,challenges have faced the unit commitment(UC)problem as a traditional power system optimization problem aiming to minimize total costs by optimally determining units’inputs and outputs,and specifying the optimal generation of each *** output power of RES such as WT and solar cells depends on natural factors such as wind speed and solar irradiation that are riddled with *** a result,the UC problem in the presence of RES faces *** grid consumed load is not always equal to and is randomly different from the predicted values,which also contributes to uncertainty in solving the aforementioned *** current study proposes a novel two-stage optimization model with load and wind farm power generation uncertainties for the security-constrained UC to overcome this *** new model is adopted to solve the wind-generated power uncertainty,and energy storage systems(ESSs)are included in the problem for further *** problem is written as an uncertain optimization model which are the stochastic nature with security-constrains which included undispatchable power resources and storage *** solve the UC programming model,a hybrid honey bee mating and bacterial foraging algorithm is employed to reduce problem complexity and achieve optimal results.

关键词： Unit commitment security-constrained programming wind farms uncertainty honey bee mating algorithm bacterial foraging algorithm

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Tourism route optimization based on improved knowledge ant colony algorithm

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COMPLEX & INTELLIGENT SYSTEMS 2022年第5期8卷 3973-3988页

作者： Li, Sidi Luo, Tianyu Wang, Ling Xing, Lining Ren, Teng Cent South Univ Forestry & Technol Sch Foreign Languages Changsha 410004 Peoples R China Cent South Univ Forestry & Technol Coll Logist & Transportat Changsha 410004 Peoples R China

With the rapid development of tourism in the economy, popular demand for tourism also increases. Unreasonable distribution arises a series of problems such as reduction of tourist satisfaction and decrease of the income in tourist attractions. Based on consideration of tourism route planning, a mathematical model which takes the maximization of the overall satisfaction of all tourist groups as the objective function is established by taking the age and preferences of tourists, the upper limits of the tourist carrying capacity in various tourism routes, etc. as constraints. It aims to maximize income in tourist attractions while improving tourist satisfaction. Based on the tourist data of a travel agency, the statistical ideas of hierarchical clustering and random sampling are utilized to process the acquired data to obtain the simulation examples in the article. Aiming at this model, a knowledge-based hybrid ant colony algorithm is designed. On this basis, the mechanism of bacterial foraging algorithm is introduced. It improves the performance of the algorithm and avoids the generation of local optimal solution. At the same time, two knowledge models are in addition to improve the solution quality of the algorithm. Typical simulation indicates that the improved ant colony algorithm can find the optimal solution at a higher efficiency when solving the tourism route planning problem. The model can also satisfy the economic benefit of enterprises and achieves favorable path optimization effect under different optional routes, thus further verifying the effect liveness of the model.

关键词： Tourism route planning Tourist satisfaction Ant colony algorithm bacterial foraging algorithm Knowledge models

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