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bacterial foraging optimization algorithm for assembly line balancing

NEURAL COMPUTING & APPLICATIONS 2014年第1期25卷 237-250页

作者： Atasagun, Yakup Kara, Yakup Selcuk Univ Dept Ind Engn Konya Turkey

Assembly line balancing is the problem of assigning tasks to workstations by optimizing a performance measure while satisfying precedence relations between tasks and cycle time restrictions. Many exact, heuristic and metaheuristic approaches have been proposed for solving simple straight and U-shaped assembly line balancing problems. In this study, a relatively new optimization algorithm, bacterial foraging optimization algorithm (BFOA), based heuristic approach is proposed for solving simple straight and U-shaped assembly line balancing problems. The performance of the proposed algorithm is evaluated using a well-known data set taken from the literature in which the number of tasks varies between 7 and 111, and results are also compared with both an ant-colony-optimization-based heuristic approach and a genetic-algorithm-based heuristic approach. The proposed algorithm provided optimal solutions for 123 out of 128 (96.1 %) test problems in seconds and is proven to be promising.

关键词： Assembly line balancing U-shaped assembly lines bacterial foraging optimization algorithm Metaheuristics

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bacterial foraging optimization algorithm for CH selection and Routing in Wireless Sensor Networks 3

Bacterial Foraging Optimization Algorithm for CH selection a...

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3rd IEEE International Conference on Recent Advances in Information Technology (RAIT)

作者： Lalwani, Praveen Das, Sagnik Indian Sch Mines Comp Sc & Engg Dhanbad Bihar India

ISBN: (纸本)9781479985791

bacterial foraging optimization algorithm (BFOA) is a widely accepted nature inspired global optimization algorithm. CH selection and Routing are well known techniques for enhancing the life of the wireless sensor networks (WSN). In two tired routing architecture, CH demises earlier due to its extra function. Therefore, proper care taken while selection of CH's. The current study focuses on solving both of the above mentioned problems using bacteria foraging algorithm. The CH selection algorithm is devised with new fitness function based on residual energy and distance. And the routing also proposed with novel fitness which considers energy and distance. The proposed algorithms are rigorously tested on different scenarios to show its performance and compared with conventional methods such as, EADC, DHCR and Hybrid Routing. Experimental results depicts that proposed algorithms performs better than existing ones.

关键词： bacterial foraging optimization algorithm WSN Cluster Head selection Routing

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bacterial foraging optimization algorithm with Quorum sensing Mechanism

Bacterial Foraging Optimization Algorithm with Quorum sensin...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Shen, Hai Zhang, Mo Shenyang Normal Univ Coll Phys Sci & Technol Shenyang Peoples R China

ISBN: (纸本)9783038351153

Quorum sensing is widely distributed in bacteria and make bacteria are similar to complex adaptive systems, with intelligent features such as emerging and non-linear, the ultimate expression of the adaptive to changes in the environment. Based on the phenomenon of bacterial quorum sensing and bacterial foraging optimization algorithm, some new optimization algorithms have been proposed. In this paper, it presents research situations, such as environment-dependent quorum sensing mechanism, quorum sensing mechanism with quantum behavior, cell-to-cell communication, multi-colony communication, density perception mechanism. Areas of future emphasis and direction in development were also pointed out.

关键词： Communication Mechanism Quorum Sensing bacterial foraging optimization algorithm

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Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using bacterial foraging optimization algorithm

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AIN SHAMS ENGINEERING JOURNAL 2016年第3期7卷 959-971页

作者： Devabalaji, K. R. Ravi, K. VIT Univ Sch Elect Engn Vellore 632014 Tamil Nadu India

In this paper, a novel approach to determine the optimal location and sizing of Distribution Generation (DG) and Distribution STATic COMpensator (DSTATCOM) is analyzed, and the objective function is formulated for minimizing power loss, operational costs and voltage profile enhancement of the system subjected to equality and in equality constraints. Loss sensitivity factor (LSF) is used to pre-determine the optimal location of DG and DSTATCOM. The bacterial foraging optimization algorithm (BFOA) is proposed to determine the optimal size of the DG and DSTATCOM. In this paper, the DG and DSTATCOM are simultaneously allocated in radial distribution system and it is analyzed with different load models. To check the feasibility, the proposed method is tested on IEEE 33-bus and 119-bus radial distribution system and the results were compared with other existing technique. (C) 2015 Ain Shams University. Production and hosting by Elsevier B.V.

关键词： bacterial foraging optimization algorithm Loss sensitivity factor Power loss minimization Voltage profile index Distribution system

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A novel bacterial foraging optimization algorithm for feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 83卷 1-17页

作者： Chen, Yu-Peng Li, Ying Wang, Gang Zheng, Yue-Feng Xu, Qian Fan, Jia-Hao Cui, Xue-Ting Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun Peoples R China

bacterial foraging optimization (BFO) algorithm is a new swarming intelligent method, which has a satisfactory performance in solving the continuous optimization problem based on the chemotaxis, swarming, reproduction and elimination-dispersal steps. However, BFO algorithm is rarely used to deal with feature selection problem. In this paper, we propose two novel BFO algorithms, which are named as adaptive chemotaxis bacterial foraging optimization algorithm (ACBFO) and improved swarming and elimination dispersal bacterial foraging optimization algorithm (ISEDBFO) respectively. Two improvements are presented in ACBFO. On the one hand, in order to solve the discrete problem, data structure of each bacterium is redefined to establish the mapping relationship between the bacterium and the feature subset. On the other hand, an adaptive method for evaluating the importance of features is designed. Therefore the primary features in feature subset are preserved. ISEDBFO is proposed based on ACBFO. ISEDBFO algorithm also includes two modifications. First, with the aim of describing the nature of cell to cell attraction-repulsion relationship more accurately, swarming representation is improved by means of introducing the hyperbolic tangent function. Second, in order to retain the primary features of eliminated bacteria, roulette technique is applied to the elimination-dispersal phase. In this study, ACBFO and ISEDBFO are tested with 10 public data sets of UCI. The performance of the proposed methods is compared with particle swarm optimization based, genetic algorithm based, simulated annealing based, ant lion optimization based, binary bat algorithm based and cuckoo search based approaches. The experimental results demonstrate that the average classification accuracy of the proposed algorithms is nearly 3 percentage points higher than other tested methods. Furthermore, the improved algorithms reduce the length of the feature subset by almost 3 in comparison to other methods

关键词： bacterial foraging optimization algorithm Feature selection Classification Support vector machine

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Optimal gait planning of bionic lower extremity exoskeleton based on elite mechanism bacterial foraging optimization algorithm

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FERROELECTRICS 2023年第1期610卷 1-18页

作者： Shenli, Wu Jing, Li Ling, Liu Yixuan, Li Xiaoming, Liang Zimeng, Yao Jun, Chen Jingqi, Pu Yingbo, Zhao Xian Univ Sch Mech & Mat Engn Xian Peoples R China Shaanxi Key Lab Surface Engn & Remfg Xian Peoples R China Xian Key Lab Intelligent Addit Mfg Technol Xian Peoples R China

To improve the stability and smoothness of bionic lower extremity exoskeleton walking gait, the elitist mechanism was introduced to enhance the diversity and improve the search ability of the optimal solution. The simulation analyses showed that the improved bacterial foraging algorithm had higher convergence accuracy, and was not easy to fall into local extremum. The improved foraging optimization algorithm was used for the optimization of the bionic lower extremity exoskeleton gait zero moment point stability margin. The simulation results showed that the optimal gait planning method proposed can increase the stability margin and ensure the stable and smooth walking.

关键词： Lower extremity exoskeleton gait optimization bacterial foraging optimization algorithm elite mechanism

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Electrochemical modeling and parameter identification based on bacterial foraging optimization algorithm for lithium-ion batteries

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JOURNAL OF APPLIED ELECTROCHEMISTRY 2016年第11期46卷 1119-1131页

作者： Ma, Yan Ru, Jingpei Yin, Mingyue Chen, Hong Zheng, Weitao Jilin Univ State Key Lab Automot Simulat & Control Changchun Peoples R China Jilin Univ Dept Control Sci & Engn Renmin St 5988 Changchun 130025 Peoples R China Jilin Univ Coll Mat Sci & Engn Renmin St 5988 Changchun 130025 Peoples R China

Batteries are key components in electric vehicles and energy storage systems. To estimate a battery's state of charge, monitor its state of health, and formulate a balanced strategy, a battery model that requires a shorter and less costly study period than a real battery is established. This paper aims to describe a single-particle model of a lithium-ion battery that has a simple structure, can be embedded in simulation software for online applications, and provides a high-accuracy characterization of the dynamics. The single-particle model, which is described by a set of partial differential equations, is a simplified electrochemical model that characterizes the dynamic voltage response. A procedure for reducing the model based on the three-parameter polynomial approximation and the volume-average integration method is proposed to simplify the partial differential equations of the single-particle model. Identifying the parameters in the battery model is the key problem. The convergent bacterial foraging optimization algorithm with a short computation time is adopted for identifying the electrochemical parameters, including the active surface areas of the electrodes, the diffusion coefficients of the lithium ions in the solid phase, and the reaction rate constants. Then, the single-particle model of a lithium-ion battery is set up in MATLAB and Simulink. Finally, the precision of the single-particle model is verified by comparing the terminal voltages of the battery and the model. The results show that the single-particle model of a lithium-ion battery is very accurate and simple, thus verifying the reliability of the parameter identification process.

关键词： Lithium-ion battery Single-particle model Three-parameter polynomial approximation Parameter identification bacterial foraging optimization algorithm

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Double Sided Optimal Bidding Strategy for Electricity Market Using Two Stage bacterial foraging optimization algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2022年第3期17卷 1499-1508页

作者： Karri, Chandram Kiran, L. Uday Raju, S. Siva Naga BITS Pilani Dept Elect Engn KK Birla Goa Campus Sancoale Goa India JNT Univ Dept Elect Engn Kakinada India JNT Univ Kakinada India

In this article, bacterial foraging optimization (BFO) algorithm is developed for double sided optimal bidding strategy in an electricity market. Optimal bidding strategy is one of the important functions in the electricity market along with forecasting of the electricity price and the profit based unit commitment. The prime objective of generating company (Genco) and distribution company (Disco) is to maximize their profit when they participate in the bidding process. Two stages are involved in the proposed approach. In the first stage, the BFO algorithm has been used to maximize the probability density function (pdf). In the second stage the BFO algorithm is again applied to maximize the profit of the GENCO and DISCO. The Proposed algorithm is developed in MATLAB (Version, 2019) and tested on standard test case available in the literature. Also, the simulation results are presented and compared. It is noticed that the proposed method yields the best results in terms of profit.

关键词： Double sided optimal bidding strategy bacterial foraging optimization algorithm IEEE 30 Bus system Marginal clearing price

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Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility

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JOURNAL OF MANUFACTURING SYSTEMS 2020年 54卷 74-93页

作者： Vital-Soto, Alejandro Azab, Ahmed Baki, Mohammed Fazle Univ Windsor Fac Engn Prod & Operat Management Res Lab Windsor ON N9B 3P4 Canada Univ Windsor Odette Sch Business Windsor ON N9B 3P4 Canada

The flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem (JSP) in which operations can be performed by a set of candidate capable machines. An extended version of the FJSP, entitled sequencing flexibility, is studied in this work, which considers precedence between the operations in the form of a directed acyclic graph instead of a sequential order. In this work, a mixed integer linear programming (MILP) formulation is presented to minimize weighted tardiness for the FJSP with sequencing flexibility. Due to the NP-hardness of the problem, a novel biomimicry hybrid bacterial foraging optimization algorithm (HBFOA) is developed, which is inspired by the behavior of E. colt bacteria in its search for food. The developed HBFOA search method is hybridized with simulated annealing (SA). Additionally, the algorithm has been enhanced by a local search method based on the manipulation of critical operations. Classical dispatching rules have been employed to create the initial swarm of HBFOA, and a new dispatching rule named minimum number of operations has been devised. The developed approach has been packaged in the form of a decision support system (DSS) developed on top of Microsoft Excel-a tool most small and mid-range enterprises (SME) use heavily for planning. A case study with local industry is presented to validate the proposed HBFOA and MILP. Additional numerical experiments using literature benchmarks are further used for validation. The results demonstrate that the HBFOA outperformed the classical dispatching rules and the best integer solution of MILP when minimizing the weighted tardiness and offered comparable results for the makespan instances.

关键词： Flexible job-shop scheduling problem Sequencing-flexibility bacterial foraging optimization algorithm Simulated annealing Weighted tardiness Decision support system

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Optimal location and sizing of capacitor placement in radial distribution system using bacterial foraging optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 71卷 383-390页

作者： Devabalaji, K. R. Ravi, K. Kothari, D. P. VIT Univ Sch Elect Engn Vellore 632014 Tamil Nadu India Gaikwad Patil Grp Inst Nagpur Nagpur Maharashtra India

This study introduces a new long term scheduling for optimal allocation of capacitor bank in radial distribution system with the objective of minimizing power loss of the system subjected to equality and in equality constraints. In the proposed method the new integrated approach of Loss Sensitivity Factor (LSF) and Voltage Stability Index (VSI) are implemented to determine the optimal location for installation of capacitor banks. bacterial foraging optimization algorithm (BFOA) is proposed to find the optimal size of the capacitor banks. The proposed method is applied on IEEE 34-bus and 85-bus radial distribution system with all possible load changes. The load is varied from light load (50%) to peak load (160%) with a step size of 1% and optimization procedure is followed to entire period. The generalized equation obtained from the curve fitting technique is very much helpful for the Distribution Network Operators (DNOs) to adjust the capacitor size according to the load changes. The simulated results demonstrate well the performance and effectiveness of the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： bacterial foraging optimization algorithm Capacitor placement Power loss minimization Loss sensitivity factor Voltage stability index

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