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A modified artificial bee colony algorithm for structural damage identification under varying temperature based on a novel objective function

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APPLIED MATHEMATICAL MODELLING 2020年 88卷 122-141页

作者： Ding, Zhenghao Fu, Kangsheng Deng, Wu Li, Jun Zhongrong, Lu Sun Yat Sen Univ Dept Appl Mech Guangzhou 510006 Peoples R China Curtin Univ Sch Civil & Mech Engn Ctr Infrastruct Monitoring & Protect Kent St Bentley WA 6102 Australia Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China

This paper presents a modified artificial bee colony algorithm for structural damage identification. Meanwhile, the effect of temperature variation is considered and the change of temperature will lead to the alteration of Young's modulus of material. A novel objective function is proposed as the combinations of the partial mode shape curvature data, alterations of natural frequencies, and a sparse penalty term. Such an objective is found to be sensitive to structural damage while not sensitive to environmental effects. On the other hand, To render the standard artificial bee colony algorithm more powerful and robustness, two local search strategies are introduced into the employed and onlooker bee phase of the artificial bee colony algorithm, respectively. Two numerical examples and a laboratory verification are employed to verify the efficiency and advantage of the proposed algorithm. The final results show that the present algorithm could yield more satisfactory identification results compared with other state-of-the-art evolutionary algorithms, even high-level noise and temperature variation are considered;and the proposed novel objective function is more sensitive to structural damages, compared with the traditional mode-shape-based objective function. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Structural damage identification artificial bee colony algorithm Modal data Measurement noise Temperature variations

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An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2019年第22期57卷 6922-6942页

作者： Li, Jun-qing Bai, Shun-Chang Duan, Pei-yong Sang, Hong-yan Han, Yu-yan Zheng, Zhi-xin Liaocheng Univ Sch Comp Sci Liaocheng Shandong Peoples R China Shandong Normal Univ Sch Informat & Engn Jinan Shandong Peoples R China Cornell Univ Dept Comp & Informat Sci Ithaca NY USA

This paper proposes an improved artificial bee colony (IABC) algorithm for addressing the distributed flow shop considering the distance coefficient found in precast concrete production system, with the minimisation of the makespan. In the proposed algorithm, each solution is first represented by a two-dimensional vector, where the first dimensional vector is the factory and the second dimensional vector lists the operation scheduling sequence of each factory. Second, considering the distributed problem feature, a distributed iterated greedy heuristic (DIG) is developed where destruction and construction processes are designed in detail while considering the distributed structures. Third, an efficient population initialisation method that considers the factory workload balance is presented. Then, a local search approach that randomly replaces two factories with two randomly selected jobs and that finds an optimal position for the two inserted operations via the DIG method is proposed. For the canonical ABC algorithm, using the DIG approach, the main three parts are improved, namely, the employee, onlooker, and scout bees. Finally, the proposed algorithm is tested on sets of extended instances based on the well-known benchmarks. Through an analysis of the experimental results, the highly effective proposed IABC algorithm is compared to several efficient algorithms drawn from the literature.

关键词： prefabricated production flow shop distributed scheduling artificial bee colony algorithm distance coefficient

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A Modified Random Forest Based on Kappa Measure and Binary artificial bee colony algorithm

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IEEE ACCESS 2021年 9卷 117679-117690页

作者： Zhang, Chen Wang, Xiaofeng Chen, Shengbing Li, Hong Wu, Xiaoxuan Zhang, Xin Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230009 Peoples R China Guochuang Software Co Ltd Hefei 230009 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230009 Peoples R China

Random forest (RF) is an ensemble classifier method, all decision trees participate in voting, some low-quality decision trees will reduce the accuracy of random forest. To improve the accuracy of random forest, decision trees with larger degree of diversity and higher classification accuracy are selected for voting. In this paper, the RF based on Kappa measure and the improved binary artificial bee colony algorithm (IBABC) are proposed. Firstly, Kappa measure is used for pre-pruning, and the decision trees with larger degree of diversity are selected from the forest. Then, the crossover operator and leaping operator are applied in ABC, and the improved binary ABC is used for secondary pruning, and the decision trees with better performance are selected for voting. The proposed method (Kappa+IBABC) are tested on a quantity of UCI datasets. Computational results demonstrate that Kappa+IBABC improves the performance on most datasets with fewer decision trees. The Wilcoxon signed-rank test is used to verify the significant difference between the Kappa+IBABC method and other pruning methods. In addition, Chinese haze pollution is becoming more and more serious. This proposed method is used to predict haze weather and has achieved good results.

关键词： Random forests Vegetation Decision trees artificial bee colony algorithm Forestry Training Radio frequency Random forest Kappa measure artificial bee colony algorithm haze prediction

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Optimal placement of elastic steel diagonal braces using artificial bee colony algorithm

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STEEL AND COMPOSITE STRUCTURES 2015年第2期19卷 349-368页

作者： Aydin, E. Sonmez, M. Karabork, T. Nigde Univ Dept Civil Engn TR-51240 Nigde Turkey Aksaray Univ Dept Civil Engn TR-68100 Aksaray Turkey

This paper presents a new algorithm to find the optimal distribution of steel diagonal braces (SDB) using artificial bee colony optimization technique. The four different objective functions are employed based on the transfer function amplitude of;the top displacement, the top absolute acceleration, the base shear and the base moment. The stiffness parameter of SDB at each floor level is taken into account as design variables and the sum of the stiffness parameter of the SDB is accepted as an active constraint. An optimization algorithm based on the artificial bee colony (ABC) algorithm is proposed to minimize the objective functions. The proposed ABC algorithm is applied to determine the optimal SDB distribution for planar buildings in order to rehabilitate existing planar steel buildings or to design new steel buildings. Three planar building models are chosen as numerical examples to demonstrate the validity of the proposed method. The optimal SDB designs are compared with a uniform SDB design that uniformly distributes the total stiffness across the structure. The results of the analysis clearly show that each optimal SDB placement, which is determined based on different performance objectives, performs well for its own design aim.

关键词： steel diagonal brace artificial bee colony algorithm structural optimization transfer functions

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Routing and wavelength assignment in optical networks using artificial bee colony algorithm

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OPTIK 2013年第12期124卷 1243-1249页

作者： Kavian, Yousef S. Rashedi, Arash Mahani, Ali Ghassemlooy, Zabih Shahid Chamran Univ Fac Engn Ahvaz Iran Iran Telecom Co Ahvaz Iran Shahid Bahonar Univ Fac Engn & Technol Kerman Iran Northumbria Univ Sch Comp Engn & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England

This paper presents an intelligent approach for modelling Routing and wavelength assignment (RWA) problem in wavelength-routed Dense-Wavelength-Division-Multiplexing (DWDM) optical networks. A new idea based on artificial bee colony (ABC) algorithm is introduced for solving RWA problem which is known to be an NP-hard problem. In the proposed ABC-RWA approach every food source represents a possible and feasible candidate lightpath between each original and destination node pair in demand matrix. The positions of food sources are modified by some artificial bees in the population where the aim is to discover the places of food sources. The food source with the highest nectar value seems to be a solution which is evaluated by the fitness function. The proposed approach is evaluated for both path length (propagation delay) and hops count optimization schemes for PAN EUROPEAN and NSFNET test bench optical networks. The performance of ABC based approach is compared with the Genetic algorithm (GA) model for solving RWA problem under random and heavy load traffic models. Simulation results demonstrate the ability and efficiency of proposed ABC model for solving RWA in real-world optical networks. Furthermore a comparison study approves that ABC is faster than GA to hit RWA global optimization solutions due to less complexity and computational processing. (C) 2012 Elsevier GmbH. All rights reserved.

关键词： Transparent optical networks DWDM technique Intelligent networks Routing and wavelength assignment artificial bee colony algorithm Genetic algorithm Optimization

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An improved artificial bee colony algorithm based on the gravity model

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INFORMATION SCIENCES 2018年 429卷 49-71页

作者： Xiang, Wan-li Meng, Xue-lei Li, Yin-zhen He, Rui-chun An, Mei-qing Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Gansu Peoples R China

artificial bee colony (ABC) algorithm is a relatively new biologically-inspired optimization algorithm. According to its solution search equation, it can be seen that ABC is good at exploration but poor at exploitation. Inspired by the gravity model, an attractive force model is proposed for choosing a better neighbor of a current individual to improve the exploitation ability of ABC. Then we propose a novel solution search equation, in Which the chosen neighbor plays an important role in guiding the searching process in the employed bee phase. Next, a random guiding search is introduced in the onlooker bee phase to balance the foregoing exploitation. Subsequently, multiple solution search equations, a scheme of perturbation frequency, and a multiple scouts search strategy in view of opposition-based learning are also incorporated into the proposed algorithm, called ABCG, to further reach a good compromise between the exploitation and the exploration. Finally, ABCG is tested on a great number of benchmark functions. The experimental results show that ABCG is effective for solving the complex benchmark problems and it can be considered as a competitive approach. (C) 2017 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Gravity model Random guiding mechanism Opposition-based learning Numerical optimization

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Cooperative artificial bee colony algorithm for multi-objective RFID network planning

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2014年 42卷 143-162页

作者： Ma, Lianbo Hu, Kunyuan Zhu, Yunlong Chen, Hanning Chinese Acad Sci Shenyang Inst Automat Lab Informat Serv & Intelligent Control Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100039 Peoples R China

Radio frequency identification (RFID) is rapidly growing into an important technology for object identification and tracking applications. This gives rise to the most challenging RFID network planning (RNP) problem in the large-scale RFID deployment environment. RNP has been proven to be an NP-hard problem that involves many objectives and constraints. The application of evolutionary and swarm intelligence algorithms for solving multi-objective RNP (MORNP) has gained significant attention in the literature, while these proposed methods always transform multi-objective RNP into single-objective problem by the weighted coefficient approach. In this work, we propose a cooperative multi-objective artificial colony algorithm called CMOABC to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP. The experiment presents an exhaustive comparison of the proposed CMOABC and two successful multi-bjective techniques, namely the recently developed multi-objective artificial bee colony algorithm (MOABC) and nondominated sorting genetic algorithm II (NSGA-II), on instances of different nature, namely the two-objective and three-objective MORNP in the large-scale RFID scenario. Simulation results show that CMOABC proves to be superior for planning RFID networks compared to NSGA-II and MOABC in terms of optimization accuracy and computation robustness. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： RFID network planning Multi-objective optimization Cooperation artificial bee colony algorithm

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Hybrid artificial bee colony algorithm for Improving the Coverage and Connectivity of Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2019年第3期108卷 1719-1732页

作者： Yue, Yinggao Cao, Li Luo, Zhongqiang Hubei Univ Arts & Sci Comp Sch Xiangyang 441053 Peoples R China Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Sichuan Peoples R China

In this paper, we studied the wireless sensor network coverage and connectivity problem. Considering the artificial bee colony algorithm existed slow convergence, easily falling into the most superior faults, then used the free search algorithm pheromone sensitivity model instead of the traditional roulette wheel selection model. We also proposed a wireless sensor network coverage and connectivity based on improved artificial bee colony algorithm. Analytical proofs and simulation experiments show that compared the random distribution of nodes, genetic algorithms with the proposed algorithm, from the coverage and the number of nodes perceived relationship, the number of nodes connectivity rate and perceived relationship, covering connectivity efficiency, the number of nodes in the network lifetime and perceived relationship, achieving the same coverage and connectivity required time-consuming aspects of WSNs coverage and connectivity issues related to study, the proposed algorithm has the higher of the coverage and connectivity, and reduce data redundancy and network traffic, improve network efficiency.

关键词： Wireless sensor networks artificial bee colony algorithm Coverage Connectivity Energy consumption

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An artificial bee colony algorithm for the economic lot scheduling problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第4期52卷 1150-1170页

作者： Bulut, Onder Tasgetiren, M. Fatih Yasar Univ Dept Ind Engn Izmir Turkey

In this study, we present an artificial bee colony (ABC) algorithm for the economic lot scheduling problem modelled through the extended basic period (EBP) approach. We allow both power-of-two (PoT) and non-power-of-two multipliers in the solution representation. We develop mutation strategies to generate neighbouring food sources for the ABC algorithm and these strategies are also used to develop two different variable neighbourhood search algorithms to further enhance the solution quality. Our algorithm maintains both feasible and infeasible solutions in the population through the use of some sophisticated constraint handling methods. Experimental results show that the proposed algorithm succeeds to find the all the best-known EBP solutions for the high utilisation 10-item benchmark problems and improves the best known solutions for two of the six low utilisation 10-item benchmark problems. In addition, we develop a new problem instance with 50 items and run it at different utilisation levels ranging from 50 to 99% to see the effectiveness of the proposed algorithm on large instances. We show that the proposed ABC algorithm with mixed solution representation outperforms the ABC that is restricted only to PoT multipliers at almost all utilisation levels of the large instance.

关键词： economic lot scheduling problem extended basic period power-of-two policy artificial bee colony algorithm variable neighbourhood search heuristic optimization

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A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem

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ENERGY CONVERSION AND MANAGEMENT 2015年 100卷 1-9页

作者： Yuan, Xiaohui Wang, Pengtao Yuan, Yanbin Huang, Yuehua Zhang, Xiaopan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Wuhan Univ Technol Sch Resource & Environm Engn Wuhan 430070 Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

This paper proposes a new artificial bee colony algorithm with quantum theory and the chaotic local search strategy (QCABC), and uses it to solve the optimal power flow (OFF) problem. Under the quantum computing theory, the QCABC algorithm encodes each individual with quantum bits to form a corresponding quantum bit string. By determining each quantum bits value, we can get the value of the individual. After the scout bee stage of the artificial bee colony algorithm, we begin the chaotic local search in the vicinity of the best individual found so far. Finally, the quantum rotation gate is used to process each quantum bit so that all individuals can update toward the direction of the best individual. The QCABC algorithm is carried out to deal with the OFF problem in the IEEE 30-bus and IEEE 118-bus standard test systems. The results of the QCABC algorithm are compared with other algorithms (artificial bee colony algorithm, genetic algorithm, particle swarm optimization algorithm). The comparison shows that the QCABC algorithm can effectively solve the OPF problem and it can get the better optimal results than those of other algorithms. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Quantum computing theory Chaos theory Optimal power flow

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