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A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization

APPLIED SOFT COMPUTING 2015年 35卷 766-785页

作者： Xiang, Yi Zhou, Yuren Sun Yat Sen Univ Sch Data Sci & Comp Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Collaborat Innovat Ctr High Performance Comp Guangzhou 510275 Guangdong Peoples R China

This paper suggests a dynamic multi-colony multi-objective artificial bee colony algorithm (DMCMOABC) by using the multi-deme model and a dynamic information exchange strategy. In the proposed algorithm, K colonies search independently most of the time and share information occasionally. In each colony, there are S bees containing equal number of employed bees and onlooker bees. For each food source, the employed or onlooker bee will explore a temporary position generated by using neighboring information, and the better one determined by a greedy selection strategy is kept for the next iterations. The external archive is employed to store non-dominated solutions found during the search process, and the diversity over the archived individuals is maintained by using crowding-distance strategy. If a randomly generated number is smaller than the migration rate R, then an elite, defined as the intermediate individual with the maximum crowding-distance value, is identified and used to replace the worst food source in a randomly selected colony. The proposed DMCMOABC is evaluated on a set of unconstrained/constrained test functions taken from the CEC2009 special session and competition in terms of four commonly used metrics EPSILON, HV, IGD and SPREAD, and it is compared with other state-of-the-art algorithms by applying Friedman test on the mean of IGD. The test results show that DMCMOABC is significantly better than or at least comparable to its competitors for both unconstrained and constrained problems. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi objective optimization Multi colony model artificial bee colony algorithm Migration strategy Friedman test

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An adaptive chaotic artificial bee colony algorithm for short-term hydrothermal generation scheduling

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期53卷 34-42页

作者： Liao, Xiang Zhou, Jianzhong Ouyang, Shuo Zhang, Rui Zhang, Yongchuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Digital Valley Sci & Technol Wuhan 430074 Hubei Peoples R China

Short-term hydrothermal scheduling (SHS) is a complicated nonlinear optimization problem with a set of constraints, which plays an important role in power system operations. In this paper, we propose to use an adaptive chaotic artificial bee colony (ACABC) algorithm to solve the SHS problem. In the proposed method, chaotic search is applied to help the artificial bee colony (ABC) algorithm to escape from a local optimum effectively. Furthermore, an adaptive coordinating mechanism of modification rate in employed bee phase is introduced to increase the ability of the algorithm to avoid premature convergence. Moreover, a new constraint handling method is combined with the ABC algorithm in order to solve the equality coupling constraints. We used a hydrothermal test system to demonstrate the effectiveness of the proposed method. The numerical results obtained by ACABC are compared with those obtained by the adaptive ABC algorithm (AABC), the chaotic ABC algorithm (CABC) and other methods mentioned in literature. The simulation results indicate that the proposed method outperforms those established optimization algorithms. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Hydrothermal scheduling artificial bee colony algorithm Adaptive Constrained optimization Swarm intelligence

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Fuzzified artificial bee colony algorithm for nonsmooth and nonconvex multiobjective economic dispatch problem

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第Sup1期21卷 1995-2014页

作者： Koodalsamy, Chandrasekaran Simon, Sishaj Pulikottil Natl Inst Technol Dept Elect & Elect Engn Pondicherry India Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli Tamil Nadu India

The economic dispatch (ED) problem is one of the important optimization problems in power system operation. Recently the power system has stressed the need for reliable, nonpolluting, and economic operation. Hence, 3 conflicting functions of reliability, emission, and fuel cost are considered in the objective function of the proposed ED problem. The problem is formulated as a nonsmooth and nonconvex problem when the valve-point effects of thermal units are considered in the proposed reliable emission and economic dispatch (REED) problem. This paper presents a multiobjective optimization methodology for solving the newly developed REED problem using a fuzzified artificial bee colony algorithm. The artificial bee colony algorithm is used to schedule the optimal dispatch and fuzzy membership approach is used to find the best compromise solution from the Pareto optimal set. The methodology is validated on an IEEE 30-bus system and 3-, 6-, 10-, 26-, and 40-unit systems and the results are compared with the existing literature. The results clearly show that the proposed method is able to produce well-distributed Pareto optimal solutions when compared with other methods reported in the literature.

关键词： artificial bee colony algorithm reliable emission and economic dispatch problem fuzzy set theory

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Optimal energy management of distributed generation in micro-grids using artificial bee colony algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第6期18卷 7402-7418页

作者： Kamarposhti, Mehrdad Ahmadi Colak, Ilhami Eguchi, Kei Islamic Azad Univ Dept Elect Engn Jouybar Branch Jouybar Iran Nisantasi Univ Fac Engn & Architectures Dept Elect & Elect Engn Istanbul Turkey Fukuoka Inst Technol Dept Informat Elect Fukuoka Japan

The use of renewable energy sources in energy distribution networks as distributed generation sources for dispersed and low consumption loads in remote areas such as remote villages and islands with low population can be a proper solution for reducing economic costs, reducing environmental pollutions and increasing energy efficiency. The purpose of this paper is optimal operation management of micro-grids by considering the existing capacities in the electricity market. In fact the microgrid operator, which is responsible for the safe operation of the network, should consider a process for planning in the network that takes into account all benefits of micro-grid's components. In other words, enough reliability for generation resources in these networks should be created in order to reduce costs and environmental pollution from energy production. In this paper, the artificial bee colony (ABC) algorithm has been used to minimize the costs and environmental pollutions by providing the optimal production power of distributed generation.

关键词： micro-grid artificial bee colony algorithm energy management distributed generations pollution cost

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Optimal Location of FACTS Devices in Order to Simultaneously Improving Transmission Losses and Stability Margin Using artificial bee colony algorithm

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IEEE ACCESS 2021年 9卷 125920-125929页

作者： Kamarposhti, Mehrdad Ahmadi Shokouhandeh, Hassan Colak, Ilhami Band, Shahab S. Eguchi, Kei Islamic Azad Univ Jouybar Branch Dept Elect Engn Jouybar Iran Semnan Univ Dept Elect Engn Semnan *** Iran Nisantasi Univ Dept Elect & Elect Engn Fac Engn & Architectures TR-25370 Istanbul Turkey Natl Yunlin Univ Sci & Technol Coll Future Future Technol Res Ctr Touliu 64002 Yunlin Taiwan Fukuoka Inst Technol Dept Informat Elect Fukuoka 8110295 Japan

It is not possible to use the full capacity of the transmission lines due to voltage limitations and stability issues. Therefore, compensators must be used to improve the transmission line capacity. One of the suggested ways for this purpose is to use flexible alternating current transmission system (FACTS) devices in the power system. The various capabilities of the FACTS devices have made it possible to set different targets to determine their optimal location and position. Some of the most important placement targets include increasing voltage stability, improving voltage profiles, reducing losses, increasing line capacity limit and reducing fuel costs for power plants through optimal power distribution. In this paper, optimal locating of FACTS devices to improve power system stability and transmission line losses reduction. Also, artificial bee colony algorithm is proposed for solving optimization problem. The artificial bee colony algorithm with high accuracy and high convergence speed is suitable for conducting FACTS placement studies. Finally, a comparison was made between the artificial bee colony algorithm and genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The results indicate that the artificial bee colony algorithm works better than the other algorithms in minimizing the fitness function.

关键词： Power system stability Thyristors Static VAr compensators artificial bee colony algorithm Power capacitors Impedance Buildings FACTS devices line loading loss reduction voltage profile artificial bee colony algorithm

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Solution of transient stability-constrained optimal power flow using artificial bee colony algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第2期21卷 360-372页

作者： Ayan, Kursat Kilic, Ulas Sakarya Univ Fac Comp & Informat Sci Dept Comp Engn Adapazari Sakarya Turkey Mehmet Akif Ersoy Univ Bucak Emin Gulmez Tech Vocat Sch Dept Elect Bucak Burdur Turkey

The transient stability constraint should be taken into consideration for the solution of the optimization problems in power systems. This paper presents a solution for the transient stability-constrained optimal power flow (TSCOPF) by a novel approach based on the artificial bee colony algorithm. The formulas of TSCOPF are derived through the addition of rotor angle inequality constraints into optimal power flow relationships. In this nonlinear optimization problem, the objective function is taken into consideration as the fuel cost of the system. The proposed approach is tested on both a WSCC 3-generator, 9-bus system and a IEEE 30-bus system, and the test results obtained are compared to prove the efficiency and effectiveness of the approach with other approaches in the literature.

关键词： artificial bee colony algorithm constrained optimization optimal power flow power system transient stability

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Dynamic Beam Scheduling of Multibeam Low Earth Orbit Satellites Based on an Enhanced artificial bee colony algorithm

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IEEE ACCESS 2022年 10卷 115424-115434页

作者： Wei, Debin Zheng, Dongdong Pan, Chengsheng Yang, Li Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Dalian Univ Commun & Network Lab Dalian 116622 Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China

The application of beam-hopping technology to low earth orbit satellites can effectively achieve flexible allocation and efficient utilization of on-board resources. Considering that the power resources on low earth orbit satellites are limited, the electromagnetic environment is complex and changeable, and the terminal distribution and service requirements are highly dynamic. We established the service model, service priority model and multibeam resource scheduling model under the constraints of beam bandwidth, on-board power, service priorities, etc. To solve the catastrophic problem of a large solution space in the resource scheduling model and to improve the convergence of the algorithm, we propose an enhanced artificial bee colony algorithm. The optimization strategy improves the process of population initialization, solution updates, and search for the global optimal solution. The simulation results show that under the constraints of cochannel interference and on-board resource utilization, the algorithm always converges to the objective function at the fastest speed, which proves that the algorithm has high applicability to the high dynamic characteristics of LEO satellites. In addition, the algorithm can obtain the global optimal solution, and thus, it can ensure the fairness of resource allocation and the effectiveness of service completion.

关键词： artificial bee colony algorithm Heuristic algorithms Resource management Dynamic scheduling Low earth orbit satellites artificial bee colony algorithm Partitioning algorithms Satellite communication Satellite communication beam hopping resource scheduling enhanced artificial bee colony algorithm

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Multilevel Thresholding Color Image Segmentation Using a Modified artificial bee colony algorithm

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2018年第8期E101D卷 2064-2071页

作者： Zhang, Sipeng Jiang, Wei Satoh, Shin'ichi Zhejiang Univ Coll Control Sci & Engn Hangzhou Zhejiang Peoples R China Natl Inst Informat Tokyo Japan

In this paper, a multilevel thresholding color image segmentation method is proposed using a modified artificial bee colony(ABC) algorithm. In this work, in order to improve the local search ability of ABC algorithm, Krill Herd algorithm is incorporated into its onlooker bees phase. The proposed algorithm is named as Krill herd-inspired modified artificial bee colony algorithm (KABC algorithm). Experiment results verify the robustness of KABC algorithm, as well as its improvement in optimizing accuracy and convergence speed. In this work, KABC algorithm is used to solve the problem of multilevel thresholding for color image segmentation. To deal with luminance variation, rather than using gray scale histogram, a HSV space-based pre-processing method is proposed to obtain 1D feature vector. KABC algorithm is then applied to find thresholds of the feature vector. At last, an additional local search around the quasi-optimal solutions is employed to improve segmentation accuracy. In this stage, we use a modified objective function which combines Structural Similarity Index Matrix (SSIM) with Kapur's entropy. The pre-processing method, the global optimization with KABC algorithm and the local optimization stage form the whole color image segmentation method. Experiment results show enhance in accuracy of segmentation with the proposed method.

关键词： color image segmentation multilevel threshokling method artificial bee colony algorithm krill herd algorithm

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Construction site layout planning using multi-objective artificial bee colony algorithm with Levy flights

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AUTOMATION IN CONSTRUCTION 2014年第Mar.期38卷 14-29页

作者： Yahya, M. Saka, M. P. Univ Bahrain Sakhir Bahrain Minist Municipal Affairs & Urban Planning Manama Bahrain Univ Bahrain Dept Civil Engn Sakhir Bahrain

Construction site layout planning has been recognized as a critical step in construction planning. The basic function of this process is to find the best arrangement of the temporary facilities according to multiple objectives that may conflict with each other and subjected to logical and resource constraints. The formulation of the construction site layout planning problem as an optimization problem turns out to be a nonlinear programming problem where there are conflicting multi-objectives to be achieved. It is shown that the swarm intelligence based meta-heuristic algorithms are quite powerful in obtaining the solution of such hard to solve type of optimization problems. In this study a multi objective artificial bee colony (MOABC) via Levy flights algorithm is proposed to determine the optimum construction site layout. The model is intended to optimize the dynamic layout of unequal-area under two objective functions. The performance of MOABC with Levy flights is demonstrated on a real benchmark construction engineering of construction site layout planning problem and the optimum solution obtained is compared with the one determined by the ant colony algorithm. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Construction site layout planning Swarm intelligence Levy flights artificial bee colony algorithm

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An artificial bee colony algorithm for the minimum routing cost spanning tree problem

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SOFT COMPUTING 2011年第12期15卷 2489-2499页

作者： Singh, Alok Sundar, Shyam Univ Hyderabad Dept Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

Given a connected, weighted, and undirected graph, the minimum routing cost spanning tree problem seeks a spanning tree of minimum routing cost on this graph, where routing cost of a spanning tree is defined as the sum of the costs of the paths connecting all possible pairs of distinct vertices in that spanning tree. This problem has several important applications in networks design and computational biology. In this paper, we have proposed an artificial bee colony (ABC) algorithm-based approach for this problem. We have compared our approach against four best methods reported in the literature-two genetic algorithms, a stochastic hill climber and a perturbation-based local search. Computational results show the superiority of our ABC approach over other approaches.

关键词： artificial bee colony algorithm Constrained optimization Heuristic Minimum routing cost spanning tree Swarm intelligence

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