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A strategy for intentional islanding of distribution networks based on node electrical relevance and artificial bee colony algorithm

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2018年第1期13卷 84-91页

作者： Zhang, Haiyan Peng, Minfang Wu, Hui Zhu, Liang Che, Hongwei Liu, Zhengyi Hunan Univ Coll Elect & Informat Engn 2 Lushan South Rd Changsha 410082 Hunan Peoples R China State Grid Hunan Elect Power Co Changsha Hunan Peoples R China

Intentional islanding is an effective approach to avoid large-area blackouts and minimize outage losses through making full use of renewable energy sources. This paper proposes a novel strategy based on node electrical relevance and artificial bee colony algorithm. The node electrical relevance is defined with equivalent electrical distance and traditional line weight, which can reflect the electrical characteristics of power systems, differentiating the proposed strategy from most existing strategies which are based on graph theory. The artificial bee colony (ABC) algorithm employed has fewer control parameters, strong robustness, and high accuracy;therefore, more loads can be incorporated into islands and restored. The constraints of load priority, load controllability, line capacity, and stable operation requirements of power systems are considered to tally with the actual distribution networks. The strategy is tested using the IEEE 69-bus distribution system and compared with other strategies from the literature. The simulation results demonstrate that the proposed strategy is more feasible and efficient. (c) 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： distribution network intentional islanding distributed generation node electrical relevance line weight artificial bee colony algorithm

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Key-node-based local search discrete artificial bee colony algorithm for obstacle-avoiding rectilinear Steiner tree construction

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NEURAL COMPUTING & APPLICATIONS 2015年第4期26卷 875-898页

作者： Zhang, Hao Ye, Dong-Yi Fuzhou Univ Ctr Discrete Math & Theoret Comp Sci Fuzhou 350108 Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Peoples R China

The obstacle-avoiding rectilinear Steiner minimal tree (OARSMT) problem is a fundamental problem in very large-scale integrated circuit physical design and can be reduced to the Steiner tree problem in graphs (GSTP), which can be solved by using three types of common methods: classic heuristics, local search algorithms, or computational intelligence algorithms. However, classic heuristics have poor solution qualities;local search algorithms easily fall into the problem of the local optimum;and the searching effects of the existing computational intelligence algorithms are poor for this problem. In order to improve the solution quality, we propose a novel discrete artificial bee colony algorithm for constructing an obstacle-avoiding rectilinear Steiner tree. We first generate the escape graph for the OARSMT problem. Then, we search for a near-optimal solution consisting of some edges of escape graph by using the discrete ABC algorithm. We apply a key-node neighborhood configuration for the local search strategy and introduce two local search operators. We then naturally use a key-node-based encoding scheme for representing the feasible solution and obtain a tight searching scope. We employ a modified classic heuristic as the encoder that can produce a feasible solution. Experiments conducted on both general GSTP and very large-scale integrated circuit instances reveal the superior performance of the proposed method in terms of the solution quality among the state-of-the-art algorithms.

关键词： NP-complete problem Obstacle-avoiding rectilinear Steiner minimal tree problem Steiner tree problem in graphs Local search artificial bee colony algorithm VLSI physical design

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A novel artificial bee colony algorithm for inverse kinematics calculation of 7-DOF serial manipulators

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SOFT COMPUTING 2019年第10期23卷 3269-3277页

作者： Zhang, Li Xiao, Nanfeng South China Univ Technol Sch Comp Sci & Engn 382 Zhonghuan Rd East Guangzhou Guangdong Peoples R China

In order to overcome the complexity in solving the inverse kinematics calculation of 7-DOF serial manipulator, a new approach CPABC based on artificial bee colony (ABC) algorithm is proposed. CPABC uses the chaotic mapping to optimize the population distribution of the initial food sources to get rid of the local optimization. The whole group of food sources in CPABC is divided into several subgroups which evolve independently and communicate with each other at a certain frequency to improve the convergence rate. To balance the global and local exploitation, two control parameters are introduced to adjust the search step and the change frequency of the optimization parameter when searching the new food source. CPABC is applied to the inverse kinematics calculation of 7-DOF serial manipulator. the simulation results show that CPABC has stronger global searching ability and more fast convergence rate than that of other ABC algorithms.

关键词： artificial bee colony algorithm Chaotic map Parallelized Inverse kinematics 7-DOF serial manipulator

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A ranking-based adaptive artificial bee colony algorithm for global numerical optimization

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INFORMATION SCIENCES 2017年 417卷 169-185页

作者： Cui, Laizhong Li, Genghui Wang, Xizhao Lin, Qiuzhen Chen, Jianyong Lu, Nan Lu, Jian Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Shenzhen Univ Coll Math & Stat Shenzhen Peoples R China

The artificial bee colony (ABC) algorithm is a powerful population-based metaheuristic for global numerical optimization and has been shown to compete with other swarm-based algorithms. However, ABC suffers from a slow convergence speed. To address this issue, the natural phenomenon in which good individuals always have good genes and thus should have more opportunities to generate offspring is the inspiration for this paper. We propose a ranking-based adaptive ABC algorithm (ARABC). Specifically, in ARABC, food sources are selected by bees to search, and the parent food sources used in the solution search equation are all chosen based on their rankings. The higher a food source is ranked, the more opportunities it will have to be selected. Moreover, the selection probability of the food source is based on the corresponding ranking, which is adaptively adjusted according to the status of the population evolution. To evaluate the performance of ARABC, we compare ARABC with other ABC variants and state-of-the-art differential evolution and particle swarm optimization algorithms based on a number of benchmark functions. The experimental results show that ARABC is significantly better than the algorithms to which it was compared. (C) 2017 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Adaptive ranking selection Global numerical optimization

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An improved artificial bee colony algorithm for robust design of power system stabilizers

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ENGINEERING COMPUTATIONS 2017年第7期34卷 2131-2153页

作者： Guesmi, Tawfik Alshammari, Badr M. Univ Hail Coll Engn Dept Elect Engn Hail Saudi Arabia

Purpose - Low-frequency oscillations of 0.1 to 3 Hz are prejudicial to the power system stability. Within this context, this study aims to present an improved artificial bee colony (ABC)-based algorithm for optimal setting of multimachine power systemstabilizers (PSSs) under several loading conditions simultaneously. Design/methodology/approach - The proposed approach symbolized by GCABC incorporates the grenade explosion technique and the Cauchy operator in the employed bee and onlooker bee phases to avoid random search. The parameters of the grenade explosion method and Cauchy operator based ABC(GCABC)based PSSs (GCABC-PSSs) are tuned to place all undamped and lightly damped electromechanical modes in a prespecified zone in the s-plan. Findings - Simulation results based on eigenvalue analysis and nonlinear time-domain simulation show the potential and the dominance of the proposed controllers GCABC-PSSs in the improvement of the system stability under several disturbances and large set of operating points compared with the classical ABC method and genetic algorithm-based PSSs. Originality/value - The novelty of the study is to efficiently implement a new optimization method called GCABC for an optimum design of PSSs. The design problem is formulated as a multi-objective optimization problem. In addition, all PSS parameters have been included in the space research.

关键词： artificial bee colony algorithm Cauchy operator Grenade explosion method Low-frequency oscillations Power system stabilizers

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Minimizing fuzzy makespan in a distributed assembly flow shop by using an efficient artificial bee colony algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 7025-7046页

作者： Zuo, Yandi Wang, Pan Fan, Zhun Li, Ming Guo, Xinhua Gao, Shijie Wuhan Univ Technol Sch Automat Wuhan Peoples R China Shantou Univ Dept Elect & Informat Engn Shantou Peoples R China Anhui Polytech Univ Sch Econ & Management Wuhu Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Peoples R China

Assembly flow shop scheduling problem (AFSP) in a single factory has attracted widespread attention over the past decades;however, the distributed AFSP with DPm -> 1 layout considering uncertainty is seldom investigated. In this study, a distributed assembly flow shop scheduling problem with fuzzy makespan minimization (FDAFSP) is considered, and an efficient artificial bee colony algorithm (EABC) is proposed. In EABC, an adaptive population division method based on evolutionary quality of subpopulation is presented;a competitive employed bee phase and a novel onlooker bee phase are constructed, in which diversified combinations of global search and multiple neighborhood search are executed;the historical optimization data set and a new scout bee phase are adopted. The proposed EABC is verified on 50 instances from the literature and compared with some state-of-the-art algorithms. Computational results demonstrate that EABC performs better than the comparative algorithms on over 74% instances.

关键词： distributed assembly flow shop scheduling uncertainty artificial bee colony algorithm fuzzy makespan

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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 artificial bee colony algorithm approach for the team orienteering problem with time windows

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COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期74卷 270-290页

作者： Cura, Tunchan Istanbul Univ Fac Business Adm Istanbul Turkey

This study addresses a highly constrained NP-hard problem called the team orienteering problem with time windows (TOPTW), which belongs to a well-known class of vehicle routing problems. This study proposes a relatively new technique called artificial bee colony (ABC) approach to solve the TOPTW. Moreover, considering that the number of studies for discrete optimization with an ABC algorithm is comparatively low, this study presents a new use of the ABC algorithm for a difficult discrete optimization problem. Additionally, this study introduces a new food source acceptance criterion and a new scout bee search behavior, both of which significantly contribute to the solution quality. The results show that the proposed method is effective, efficient, and comparable to other approaches. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Heuristics Routing Team orienteering problem Time window artificial bee colony algorithm

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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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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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