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Solving fuzzy scheduling using clustering method and bacterial foraging algorithm

SOFT COMPUTING 2023年第11期27卷 7285-7297页

作者： Li, Yingli Wang, Jiahai Gao, Jianxiang Liu, Zhengwei Tongji Univ Sch Mech Engn 4800 Caoan Rd Shanghai 201804 Peoples R China

Multi-objective fuzzy flexible job-shop scheduling problem (MFFJSSP) is a combination of multi-objective fuzzy scheduling and flexible job shop scheduling, which has higher complexity and is an NP-hard problem. For incompletely automated job shop, MFFJSSP is more important and conforms to the actual production. To solve the MFFJSSP, an effective multi-objective fuzzy scheduling method was proposed. First, a fuzzy number processing method based on fuzzy clustering that is more in line with the actual production was designed to replace the classic triangular fuzzy number. Second, based on the critical path theory, a more efficient method of critical operation block selection and critical operation movement were designed and applied them to the chemotaxis stage of bacterial foraging optimization algorithm to improve the accuracy of optimization and reduce invalid movement. Third, a novel decision tree-based bacterial reproduction method was designed to prevent the algorithm from falling into a local optimal solution. Finally, some cases were generated to verify the feasibility and effectiveness of the proposed algorithm from multiple dimensions.

关键词： Flexible job shop Multi-objective optimization bacterial foraging algorithm Fuzzy scheduling

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An adaptive rejuvenation of bacterial foraging algorithm for global optimization

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第2期82卷 1965-1993页

作者： Khosla, Tejna Verma, Om Prakash Delhi Technol Univ Dept Comp Sci & Engn Delhi India Delhi Technol Univ Dept Elect & Commun Engn Delhi India

bacterial foraging algorithm (BFA) is a novel nature-inspired algorithm that mimics the social foraging behavior of E. coli. Bacteria. However, it gets stuck in the local optima trap and yields poor convergence in complex landscapes. To improve the exploration-exploitation balance and achieve the global optima quickly, this paper proposes a novel hybrid called the bacterial foraging algorithm-firefly algorithm (BFA-FA). In this work, two strategies namely adaptive strategy and leadership strategy are applied on conventional BFA. The performance is examined on standard, non-linear and CEC_2017 benchmark functions over several evaluation parameters. The results on benchmark functions show that BFA-FA provides accurate solutions, avoids local optima, works well on multimodal and multidimensional landscapes, and converges faster. It also shows the statistically significant difference among other algorithms. The proposed algorithm is applied on two classical engineering problems to validate its robustness and applicability.

关键词： Adaptation bacterial foraging algorithm Firefly algorithm Hybridization Optimization

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bacterial foraging algorithm for a Neural Network Learning Improvement in an Automatic Generation Controller

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ENERGIES 2023年第6期16卷 2802页

作者： Al-Majidi, Sadeq D. Altai, Hisham Dawood Salman Lazim, Mohammed H. Al-Nussairi, Mohammed Kh. Abbod, Maysam F. Al-Raweshidy, Hamed S. Univ Misan Coll Engn Dept Elect Engn Amarah 62001 Iraq Brunel Univ London Coll Engn Dept Elect & Elect Engn London UB8 3PH England

The frequency diversion in hybrid power systems is a major challenge due to the unpredictable power generation of renewable energies. An automatic generation controller (AGC) system is utilised in a hybrid power system to correct the frequency when the power generation of renewable energies and consumers' load demand are changing rapidly. While a neural network (NN) model based on a back-propagation (BP) training algorithm is commonly used to design AGCs, it requires a complicated training methodology and a longer processing time. In this paper, a bacterial foraging algorithm (BF) was employed to enhance the learning of the NN model for AGCs based on adequately identifying the initial weights of the model. Hence, the training error of the NN model was addressed quickly when it was compared with the traditional NN model, resulting in an accurate signal prediction. To assess the proposed AGC, a power system with a photovoltaic (PV) generation test model was designed using MATLAB/Simulink. The outcomes of this research demonstrate that the AGC of the BF-NN-based model was effective in correcting the frequency of the hybrid power system and minimising its overshoot under various conditions. The BP-NN was compared to a PID, showing that the former achieved the lowest standard transit time of 5.20 s under the mismatching power conditions of load disturbance and PV power generation fluctuation.

关键词： automatic generation controller neural network model bacterial foraging algorithm hybrid power system photovoltaic power generation

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Construction of supply chain coordination and optimization model of fresh food e-commerce platform based on improved bacterial foraging algorithm

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RAIRO-OPERATIONS RESEARCH 2022年第6期56卷 3853-3869页

作者： He, Juan Xian Univ Finance & Econ Sch Informat Xian 710100 Shaanxi Peoples R China

In order to maximize the overall profit of the supply chain of fresh food e-commerce platform, the supply chain coordination and optimization model of fresh food e-commerce platform based on the improved bacterial foraging algorithm is constructed. The basic model of bacterial foraging algorithm is constructed through chemotaxis, reproduction, elimination & dispersal, and the bacterial foraging algorithm is improved by using four parts: bacterial individual and parameter initialization, chemotaxis behavior, reproduction behavior and migration behavior, so as to realize the coordination and optimization of the supply chain of fresh food e-commerce platform. Use the Internet service platform to promote the electronization of the supply chain transaction process and improve the overall operation efficiency. Through the cooperation among fresh food suppliers, fresh food e-commerce and upstream fresh food suppliers, the supply chain coordination and optimization model of fresh food e-commerce platform is constructed to improve the overall profit of the supply chain. The experimental results show that using the improved bacterial foraging algorithm to solve the supply chain coordination and optimization model of fresh food e-commerce platform has high effectiveness, and can maximize the overall profit of the supply chain of fresh food e-commerce platform.

关键词： bacterial foraging algorithm fresh food e-commerce platform supply chain coordination

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Hybrid spiral-bacterial foraging algorithm for a fuzzy control design of a flexible manipulator

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2022年第1期41卷 340-358页

作者： Kasruddin Nasir, Ahmad Nor Ahmad, Mohd Ashraf Tokhi, M. Osman Univ Malaysia Pahang Fac Elect & Elect Engn Technol Pekan Campus Pekan 26600 Pahang Malaysia London South Bank Univ Sch Engn Dept Elect & Elect Engn London England

A novel hybrid strategy combining a spiral dynamic algorithm (SDA) and a bacterial foraging algorithm (BFA) is presented in this article. A spiral model is incorporated into the chemotaxis of the BFA algorithm to enhance the capability of exploration and exploitation phases of both SDA and BFA with the aim to improve the fitness accuracy for the SDA and the convergence speed as well as the fitness accuracy for BFA. The proposed algorithm is tested with the Congress on Evolutionary Computation 2013 (CEC2013) benchmark functions, and its performance in terms of accuracy is compared with its predecessor algorithms. Consequently, for solving a complex engineering problem, the proposed algorithm is employed to obtain and optimise the fuzzy logic control parameters for the hub angle tracking of a flexible manipulator system. Analysis of the performance test with the benchmark functions shows that the proposed algorithm outperforms its predecessor algorithms with significant improvements and has a competitive performance compared to other well-known algorithms. In the context of solving a real-world problem, it is shown that the proposed algorithm achieves a faster convergence speed and a more accurate solution. Moreover, the time-domain response of the hub angle shows that the controller optimised by the proposed algorithm tracks the desired system response very well.

关键词： bacterial foraging algorithm flexible manipulator system PD-like fuzzy logic control spiral dynamic

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Research on Cargo Loading Strategy of Container Ship Based on bacterial foraging algorithm 10

Research on Cargo Loading Strategy of Container Ship Based o...

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10th International Conference on Traffic and Logistic Engineering (ICTLE)

作者： Zhang, You Tian Zhe Jiang Gong Shang Univ Coll Business Adm Hangzhou Zhejiang Peoples R China

ISBN: (数字)9781665486309

ISBN: (纸本)9781665486309

Under the background of frequent international exchanges and the rapid development of international logistics, the cost optimization of international marine transportation as the main mode of transportation of goods has become more and more important. Under the premise of ensuring the safety and stability, the transformable container ship can improve the space utilization of cargo ships with maximum efficiency, thereby reducing the number of rounds or increasing the revenue to optimize the cost. The research method of this paper uses the differentiation of large and small cargoes based on the rules of determining the order, positioning and three space segmentation. Priority is given to loading small cargoes into containers, and then loading large cargoes and containers into the cargo hold. Finally, the remaining cargo is loaded onto the deck to maximize space utilization. Using genetic coding expressed in loading order, the way of loading cargo is coded to obtain the solution of the fitness function, and then an adaptive step size bacterial foraging algorithm is used to derive an optimal solution. Optimizing cargo ship loading strategy by designing model algorithms is of certain practical significance for the development of transnational logistics transportation activities.

关键词： transformable container ship three-dimensional crating three-space segmentation rule bacterial foraging algorithm

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Knowledge worker scheduling optimization model based on bacterial foraging algorithm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 124卷 330-337页

作者： Dan, Yufang Tao, Jianwen Ningbo Polytech Inst Artificial Intelligence Applicat Ningbo 315800 Peoples R China

bacterial foraging algorithm comes from the best survival selection mechanism of animals in nature. As the representative of the heuristic algorithm, the bacterial foraging algorithm has unique advantages in solving the multi difficulty scheduling problem effectively. In order to realize the artificial intelligent management of the enterprise's staff scheduling, this paper constructs the knowledge staff scheduling model by using a bacterial foraging algorithm and analyzes the implementation principle, advantages, and disadvantages of the algorithm. The influence of the basic parameters in the algorithm model on the algorithm performance is analyzed. In order to optimize the unconventional foraging strategy, the improvement measures of bacterial foraging behavior were proposed. Finally, the performance of the optimized bacterial foraging algorithm is tested and compared with the basic bacterial foraging algorithm, genetic algorithm, and particle swarm optimization algorithm. The experimental results show that the optimized bacterial foraging algorithm can achieve better convergence accuracy and shorter convergence speed for the objective function, and it can solve the scheduling optimization problem of knowledge workers more quickly, accurately, and effectively. The research in this paper shows that the optimization of four aspects of the basic bacterial foraging algorithm improves the performance of the algorithm and provides a theoretical reference for the optimization of the bacterial foraging algorithm. (C) 2021 Elsevier B.V. All rights reserved.

关键词： bacterial foraging algorithm Multi difficulty scheduling problem Artificial intelligence management Convergence accuracy Convergence rate Enterprise employee scheduling

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Research on electrical load distribution using an improved bacterial foraging algorithm

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Zhang, Yi Lv, Yang Jilin Jianzhu Univ Changchun Peoples R China

This paper proposes an improved bacterial foraging algorithm for electrical load distribution to impro-ve power plants' efficiency and reduce energy consumption costs. In the chemotaxis stage, the adaptive step size is introduced to accelerate the random search speed compared with the traditional algorithm. In the replication stage, a hybrid crisscross operator is proposed to replace the traditional binary replication method in the algorithm to ensure the diversity of the population and improve the efficiency of the algorithm. The adaptive dynamic probability is used instead of the initial fixed probability to improve the global search performance of the algorithm. The mathematical model of electrical load distribution in a natural power plant is established, and the improved bacterial foraging algorithm is used to solve the model. Through comparative analysis of two power plant unit experiments, it is proved that the results of the improved algorithm can reduce 3.671% and 1.06% respectively compared with the particle swarm optimization algorithm, and 7.26% and 1.37% respectively compared with the traditional bacterial foraging algorithm, which can significantly reduce the coal consumption of the power plant.

关键词： bacterial foraging algorithm crisscross operator electrical load distribution economic benefits of power plant self-adaption

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Generation-based automatic generation control with multisources power system using bacterial foraging algorithm

ENGINEERING REPORTS

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ENGINEERING REPORTS 2020年第8期2卷

作者： Hakimuddin, Nizamuddin Nasiruddin, Ibraheem Bhatti, Terlochan Singh New Okhla Ind Dev Author Noida 201301 Uttar Pardesh India Jamia Millia Islamia Fac Engn & Technol New Delhi India Indian Inst Technol Delhi Ctr Energy Studies Hauz Khas Delhi India

This article presents an application of bacterial foraging algorithm (BFA) for design and implementation of generation-based PID structured automatic generation control (AGC) in a 2-area multisources power system with hydro, thermal, and gas power plants incorporated in each area. Most of AGC studies carried out so far have considered the initial system loading to be equal to 50% of the area generation capacity. But, AGC controller parameters are uncertain due to stochastic nature of power demand of the end users. Hence, in this article, the design of AGC controller is proposed on the basis of generation schedule by incorporating changes in power system gain constant, power system time constant, frequency bias constant, and so on. The dynamic responses of power system with BFA tuned AGC controller are compared with the genetic algorithm tuned AGC controller. The parameters of the controllers are evaluated by using these techniques and investigations are carried out to find the best performance of the system. Therefore, it is desirable to find the parameters of the generation-based controller depending upon the contribution of its constituent hydro, thermal, and gas energy sources in the total power generation.

关键词： automatic generation control multisources power system generation-based controller bacterial foraging algorithm

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Research on Health Big Data Preprocessing Technology Based on AHSMM and bacterial foraging algorithm 2

Research on Health Big Data Preprocessing Technology Based o...

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IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Liu, Zhen Liang, Xu Huang, Ming Dalian Jiaotong Univ Dalian 116052 Peoples R China

ISBN: (纸本)9781538692981

With the development of big data technology and the popularity of Internet of Things technology, the demand for management is becoming more and more serious. In order to solve the problem of health big data preprocessing technology, the method of Adaptive Hidden Semi-Markov Model (AHSMM) based on bacterial foraging algorithm is proposed. First of all, the bacterial foraging algorithm referring health big data is used to apply the equipment health diagnosis and prediction methods to the actual case of the United States Caterpillar company's standard health prediction experiments. Next, the proposed method is applied to the actual case of hydraulic pump of Caterpillar company. The results verify the effectiveness and stability of the proposed algorithm.

关键词： health big data bacterial foraging algorithm AHSMM

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