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An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training

APPLIED SOFT COMPUTING 2016年 49卷 423-436页

作者： Karaboga, Dervis Kaya, Ebubekir Erciyes Univ Dept Comp Engn Fac Engn TR-38039 Kayseri Turkey Nevsehir Haci Bektas Veli Univ Nevsehir Vocat Coll Dept Comp Technol TR-50300 Nevsehir Turkey

In this study, we propose an Adaptive and Hybrid artificial bee colony (aABC) algorithm to train ANFIS. Unlike the standard ABC algorithm, two new parameters are utilized in the solution search equation. These are arithmetic crossover rate and adaptivity coefficient. aABC algorithm gains the rapid convergence feature with the usage of arithmetic crossover and it is applied on two different problem groups and its performance is measured. Firstly, it is performed over 10 numerical 'benchmark functions'. The results show that aABC algorithm is more efficient than standard ABC algorithm. Secondly, ANFIS is trained by using aABC algorithm to identify the nonlinear dynamic systems. Each application begins with the randomly selected initial population and then average RMSE is obtained. For four examples considered in ANFIS training, train error values are respectively computed as 0.0344, 0.0232, 0.0152 and 0.0205. Also, test error values for these examples are respectively found as 0.0255, 0.0202, 0.0146 and 0.0295. Although it varies according to the examples, performance increase between 4.51% and 33.33% occurs. Additionally, it is seen that aABC algorithm converges bettter than ABC algorithm in the all examples. The obtained results are compared with the neuro-fuzzy based approaches which are commonly used in the literature and it is seen that the proposed ABC variant can be efficiently used for ANFIS training. (C) 2016 Elsevier B.V. All rights reserved.

关键词： ANFIS Neuro-fuzzy artificial bee colony algorithm Swarm intelligence Nonlinear system identification

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Networked correlation-aware manufacturing service supply chain optimization using an extended artificial bee colony algorithm

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APPLIED SOFT COMPUTING 2019年 76卷 121-139页

作者： Zhang, Shuai Xu, Song Huang, Xiaoling Zhang, Wenyu Chen, Mingzhou Zhejiang Univ Finance & Econ Sch Informat 18 Xueyuan St Hangzhou 310018 Zhejiang Peoples R China Nankai Univ Business Sch 94 Weijin Rd Tianjin 300071 Peoples R China Zhejiang Univ Finance & Econ Sch Int Educ 18 Xueyuan St Hangzhou 310018 Zhejiang Peoples R China

Manufacturing service supply chain (MSSC) optimization has been intensively studied to find an optimal service composition solution with the best quality of service (QoS) value. However, traditional MSSC optimization methods usually assume that candidate services are independent of one another. Therefore, potentially better MSSC solutions may have been neglected by not considering the positive influence of correlations between services on the QoS value. This study proposes a novel networked correlation-aware manufacturing service composition (NCMSC) mathematical model to characterize the influence of vertical and horizontal correlations between services on the QoS value of MSSC solution. To solve the NCMSC model, an extended artificial bee colony (ABC) algorithm is proposed to find a near-optimal solution with the best QoS value. The specific improvements to the original ABC algorithm include the following: (1) a new matrix-based encoding scheme is proposed to describe the MSSC solution in which each column contains a vertical composite structure and collaborative services for each subtask;(2) the migration operator of a biogeography-based optimization algorithm is combined with the original ABC algorithm to address the discrete MSSC optimization problem and improve the performance of the original ABC algorithm. The results of the experiments illustrate the importance of networked correlations between services, better practicality, effectiveness, and efficiency of the extended ABC algorithm in solving the optimization problem of MSSC. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Manufacturing service supply chain Manufacturing service composition Networked correlations between services artificial bee colony algorithm

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Fractional-order artificial bee colony algorithm with application in robot path planning

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期306卷 47-64页

作者： Cui, Yibing Hu, Wei Rahmani, Ahmed Cent Lille CRIStAL UMR CNRS 9189 F-59651 Villeneuve Dascq France Beijing Jiaotong Univ Inst Syst Sci Beijing 100044 Peoples R China

artificial bee colony (ABC) algorithm is a popular meta-heuristic optimization algorithm inspired by the foraging behaviors of honeybees. Although ABC has outstanding exploration ability, it has certain weak-ness like poor exploitation capability and low solution precision. Therefore, to tackle these weakness and improve the performance, an ABC algorithm based on fractional-order calculus (FOABC) is proposed. More specially, the memory properties of fractional-order calculus is incorporated into onlooker bee phase to improve the local-search capability. Each time generating a new candidate solution, the previous foraging behaviors stored in memory are referred. Another enhanced search strategy is adopted in employed bee phase to avoid losing the balance between diversification and intensification. To evaluate the performance of FOABC, several groups of experiments are conducted on CEC 2017 benchmark problems. According to the experimental studies, FOABC is outstanding in comparisons with ABC variants and also performs re-markably well in practical applications, namely robot path planning.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Global optimization artificial bee colony algorithm Fractional calculus L?vy distribution Robot path planning

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Optimization of a modified double-turbine Kalina cycle by using artificial bee colony algorithm

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APPLIED THERMAL ENGINEERING 2015年 91卷 19-32页

作者： Sadeghi, Sadegh Saffari, Hamid Bahadormanesh, Nikrouz Iran Univ Sci & Technol Sch Mech Engn Tehran Narmak Iran

Owing to appropriate performance of ammonia-water as a working fluid over two-phase region when exploiting low-temperature heat sources, a modified low-temperature double-turbine Kalina cycle system is designed to boost thermal efficiency. Due to low pressure after the second turbine, it is not affordable to use more than two turbines. Input mass flow rate of the second turbine is supplied by adding heat to the output liquid ammonia-water mixture from the first separator, separating the vapor at the outlet of the first turbine before blending the two streams. In order to reach the optimum thermal efficiency of the cycle, ABC (artificial bee colony) algorithm is implemented as a novel powerful multi-variable optimization algorithm. Considering the structure of the algorithm, convergence speed and accuracy of solutions have been considerably enhanced when compared to those of GA, PSO and DE algorithms. Such a relative enhancement is indicated by limit parameter and reducing probability of occurrence of local optimum problem. In this paper, thermal efficiency is selected as the objective function of ABC algorithm where its optimum value for the suggested Kalina cycle is found to be 26.32%. Finally, effects of the first separator inlet pressure and temperature, basic ammonia mass fraction and mass flow rate of the ammonia-water working fluid on net power output, required heat energy for the cycle and thermal efficiency are investigated. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Kalina cycle artificial bee colony algorithm Ammonia-water mixture Therthal efficiency

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Hybrid artificial bee colony algorithm for a Parallel Batching Distributed Flow-Shop Problem With Deteriorating Jobs

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IEEE TRANSACTIONS ON CYBERNETICS 2020年第6期50卷 2425-2439页

作者： Li, Jun-Qing Song, Mei-Xian Wang, Ling Duan, Pei-Yong Han, Yu-Yan Sang, Hong-Yan Pan, Quan-Ke Shandong Normal Univ Sch Informat & Engn Jinan 250014 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Shanghai Univ Coll Mech & Elect Engn & Automat Shanghai 200444 Peoples R China

In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.

关键词： artificial bee colony algorithm Production facilities Job shop scheduling Optimization Parallel machines artificial bee colony (ABC) deteriorating job distributed flow shop (DFS) parallel batching

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Buckling load optimization of laminated plates via artificial bee colony algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2014年第4期52卷 755-765页

作者： Topal, Umut Ozturk, Hasan Tahsin Karadeniz Tech Univ Fac Technol Dept Civil Engn TR-61830 Trabzon Turkey

In this present work, artificial bee colony algorithm (ABCA) is used to optimize the stacking sequences of simply supported antisymmetric laminated composite plates with criticial buckling load as the objective functions. The fibre orientations of the layers are selected as the optimization design variables with the aim to fmd the optimal laminated plates. In order to perform the optimization based on the ABCA, a special code is written in MATLAB software environment. Several numerical examples are presented to illustrate this optimization algorithm for different plate aspect ratios, number of layers and load ratios.

关键词： laminated plates artificial bee colony algorithm buckling optimization

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Time delay control of cable-driven manipulators with artificial bee colony algorithm

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TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2018年第2期42卷 177-186页

作者： Yan, Fei Wang, Yaoyao Xu, Wei Chen, Bai Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Zhejiang Peoples R China

Cable-driven manipulators (CDM) are widely-used for their unique advantages such as light weight, low moving mass, high payload-to-weight ratio, and large reachable workspace. However, their complex dynamic character and low stiffness with flexible joints make the control design much more difficult than for traditional robot manipulators. In this paper, time delay control (TDC), which combines the proportional-integral-derivative (PID) control method and time delay estimation (TDE) technology, will be investigated to build a model-free controller for CDM. PID parameters are reduced dramatically as TDE compensates for a large proportion of unknown dynamics. To handle the problem in tuning parameters of this controller, artificial bee colony (ABC) algorithm is utilized to obtain optimal parameters of PID. Finally, simulations are conducted to verify the effectiveness of the propose controller and the tuning method.

关键词： cable-driven manipulator PID time delay estimation artificial bee colony algorithm

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A Multi-Species artificial bee colony algorithm and Its Application for Crowd Simulation

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IEEE ACCESS 2019年 7卷 2549-2558页

作者： Wang, Shouna Liu, Hong Gao, Kaizhou Zhang, Jianxin Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China Shandong Prov Key Lab Distributed Comp Software N Jinan 250014 Shandong Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

The artificial bee colony (ABC) algorithm has the problem of slow convergence and may be trapped into local optimum. In this paper, a multi-species ABC (MABC) algorithm is proposed based on the multi-swarm model. The MABC algorithm uses dynamic segmentation of the swarm and a co-evolution strategy. The strategy of dynamic segmentation divides the colony into multiple sub-species, and the species communicate with each other using the co-evolution strategy. The combined global communication pattern and local communication pattern are applied among sub-species. In order to test the performance of the algorithm, experiments are conducted on the CEC'05 Test Functions. To test the performance of the algorithm in crowd simulation for evacuation, we simulate it through a crowd simulation system for evacuation, and the MABC algorithm improves the efficiency of crowd evacuation.

关键词： artificial bee colony algorithm dynamic strategy of segmentation co-evolution strategy function optimization problems crowd simulation for evacuation

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Modeling and simulation of improved artificial bee colony algorithm with data-driven optimization

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SIMULATION MODELLING PRACTICE AND THEORY 2019年 93卷 305-321页

作者： Zhang, Mei Tan, Ying-Tong Zhu, Jin-Hui Chen, Yi-Nong Liu, Hai-Ming South China Univ Technol Sch Automat Sci & Control Engn Guangzhou 510640 Guangdong Peoples R China South China Univ Technol Sch Software Guangzhou 510640 Guangdong Peoples R China Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85287 USA

To balance the exploration and exploitation and to enhance the convergence rate of an artificial bee colony (ABC) algorithm, the driving force of using additional data during searching process is studied in this paper, and an improved ABC algorithm with data-driven optimization (DDABC) is proposed. First, to speed up convergence rate, the searching process is driven by directional guiding data. Therefore, a bee colony would learn from the directional guiding data, instead of picking up a random direction. Second, to enhance the exploitation capability of the onlooker bees, the searching process is driven by local data of onlooker bees. Every onlooker bee would search independently for multiple times to generate local data applied into optimization. Comparisons are made with a number of other ABC-based and nature-inspired algorithms. The results show that the proposed DDABC achieves improvements in both exploitation capability and convergence rates.

关键词： artificial bee colony algorithm Swarm intelligence Data driven Optimization

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Training ANFIS by using the artificial bee colony algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第3期25卷 1669-1679页

作者： Karaboga, Dervis Kaya, Ebubekir Erciyes Univ Dept Comp Engn Fac Engn Kayseri Turkey Nevsehir Haci Bektas Veli Univ Nevsehir Vocat Coll Dept Comp Technol Nevsehir Turkey

In this study, a new adaptive network-based fuzzy inference system (ANFIS) training algorithm, the artificial bee colony (ABC) algorithm, is presented. Antecedent and conclusion parameters existing in the structure of ANFIS are optimized with the ABC algorithm and ANFIS training is realized. Identification of a set of nonlinear dynamic systems is performed in order to analyze the suggested training algorithm. The ABC algorithm is operated 30 times for each identification case and the average root mean square error (RMSE) value is obtained. Training RMSE values calculated for the four examples considered are 0.0325, 0.0215, 0.0174, and 0.0294, respectively. In addition, test error values for the same cases are respectively computed as 0.0270, 0.0186, 0.0167, and 0.0435. The results obtained are compared with those of known neuro-fuzzy-based methods frequently used in the literature in identification studies of nonlinear systems. It is shown that ANFIS can be trained successfully by using the ABC algorithm for the identification of nonlinear systems.

关键词： ANFIS swarm intelligence artificial bee colony algorithm nonlinear system identification

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