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An improved many-objective artificial bee colony algorithm for cascade reservoir operation

NEURAL COMPUTING & APPLICATIONS 2023年第18期35卷 13613-13629页

作者： Wang, Hui Wang, Shuai Wei, Zichen Zeng, Tao Ye, Tingyu Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Peoples R China

artificial bee colony (ABC) has shown good performance on single-objective and ordinary multi-objective optimization problems. However, ABC faces some difficulties with increasing number of objectives. The selection pressure based on Pareto dominance degrades severely. The original ABC shows weak exploitation ability and slow convergence speed. To help ABC solve many-objective optimization problems (MaOPs), this paper proposes an improved many-objective ABC algorithm based on decomposition and dimension learning (called MaOABC-DDL). Firstly, an MaOP is converted to several sub-problems by the decomposition. The original fitness function is not available because of multiple objective values. Then, a new fitness function is defined based on the ranking of each objective. Solutions with good fitness values are selected to form an elite set. To improve the convergence, an elite set guided search strategy and dimension learning are designed for the employed bee and onlooker bee stages, respectively. Moreover, the scout bee stage is modified to dynamically allocate computing resources. To verify the performance of MaOABC-DDL, the DTLZ and MaF benchmark problems with 3, 5, 8, and 15 objectives are tested. Results show that MaOABC-DDL can obtain better performance when compared with seven other many-objective evolutionary algorithms. Finally, MaOABC-DDL is applied to cascade reservoir operation. Simulation results show that our approach still achieves promising performance.

关键词： artificial bee colony algorithm Swarm intelligence Many-objective optimization Cascade reservoir operation

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Inverse analysis of geomechanical parameters by the artificial bee colony algorithm and multi-output support vector machine

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2016年第7期24卷 1266-1281页

作者： Zhao, Hongbo Yin, Shunde Henan Polytech Univ Sch Civil Engn Jiaozuo Peoples R China Univ Wyoming Dept Chem & Petr Engn Laramie WY 82071 USA

Geomechanical parameters play a very important role in geotechnical engineering design and construction. Inverse analysis provides a powerful tool to characterize geomechanical parameters based on the behaviour of rock under certain boundary conditions. In this paper, a new method for inverse analysis that takes advantage of multi-output support vector machine (MSVM) and artificial bee colony (ABC) is proposed and applied to solving a real field problem. MSVM is used to map the relationship between geomechanical parameters and displacements. ABC is adopted in inverse analysis to find the optimal geomechanical parameters. The proposed method is used to recognize the parameters of the permanent shiplock slope of the Three Gorges in China. Results show that the proposed method can effectively determine geomechanical parameters.

关键词： Inverse analysis parameters identification geotechnical engineering multi-output support vector machine artificial bee colony algorithm

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DisABC: A new artificial bee colony algorithm for binary optimization

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APPLIED SOFT COMPUTING 2012年第1期12卷 342-352页

作者： Kashan, Mina Husseinzadeh Nahavandi, Nasim Kashan, Ali Husseinzadeh Tarbiat Modares Univ Dept Ind Engn Fac Engn Tehran Iran

artificial bee colony (ABC) algorithm is one of the recently proposed swarm intelligence based algorithms for continuous optimization. Therefore it is not possible to use the original ABC algorithm directly to optimize binary structured problems. In this paper we introduce a new version of ABC, called DisABC, which is particularly designed for binary optimization. DisABC uses a new differential expression, which employs a measure of dissimilarity between binary vectors in place of the vector subtraction operator typically used in the original ABC algorithm. Such an expression helps to maintain the major characteristics of the original one and is respondent to the structure of binary optimization problems, too. Similar to original ABC algorithm, DisABC's differential expression works in continuous space while its consequence is used in a two-phase heuristic to construct a complete solution in binary space. Effectiveness of DisABC algorithm is tested on solving the uncapacitated facility location problem (UFLP). A set of 15 benchmark test problem instances of UFLP are adopted from OR-Library and solved by the proposed algorithm. Results are compared with two other state of the art binary optimization algorithms, i.e., binDE and PSO algorithms, in terms of three quality indices. Comparisons indicate that DisABC performs very well and can be regarded as a promising method for solving wide class of binary optimization problems. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Binary optimization artificial bee colony algorithm Swarm intelligence Dissimilarity measure of binary structures Uncapacitated facility location problem

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Application of the artificial bee colony algorithm-based projection pursuit method in statistical rock mass stability estimation

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ENVIRONMENTAL EARTH SCIENCES 2013年第8期68卷 2337-2345页

作者： Li, Haojin Li, Junjie Kang, Fei Dalian Univ Technol Dept Hydraul Engn Fac Infrastruct Engn Dalian City Liaoning Provin Peoples R China

Because of the complexity of factors that affect rock mass stability, the design and decision-making in related engineering cannot rely solely on theoretical analysis and numerical calculation, but depend on the comprehensive judgment of experts. In pursuit of a statistical approach that may improve this disparity, an artificial bee colony algorithm-based projection pursuit (ABC-PP) method is presented for rock mass stability determination. The ABC-PP method is a powerful tool to deal with high-dimension problems, which characterize rock mass stability assessment practice. Two experiments are employed to demonstrate the efficiency of the ABC-PP method. In the first case, the state of stability is classified at two levels: stable and failed, whereas in the second case stability is classified at five levels (1-5) to test the capability of multi-level prediction of the ABC-PP method. Results show that the ABC-PP method could predict the rock mass stability accurately and may also provide the relative importance of specific controls on stability.

关键词： Rock mass stability estimation Projection pursuit method artificial bee colony algorithm Multi-factor comprehensive evaluation

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A Hybrid Differential artificial bee colony algorithm based tuning of fractional order controller for Permanent Magnet Synchronous Motor drive

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2014年第3期5卷 327-337页

作者： Rajasekhar, Anguluri Abraham, Ajith Pant, Millie Natl Inst Technol Dept Elect & Elect Engn Warangal 506004 Andhra Pradesh India Tech Univ Ostrava VSB Fac Comp Sci & Elect Engn Ostrava Czech Republic Machine Intelligence Res Labs MIR Labs Auburn WA USA Indian Inst Technol Dept Paper & Pulp Technol Roorkee 247667 Uttar Pradesh India

In this paper a novel Hybrid Differential artificial bee colony algorithm (HDABCA) has been proposed for designing a fractional order proportional-integral (FO-PI) speed controller in a Permanent Magnet Synchronous Motor (PMSM) drive. FO-PI controllers' parameters involve proportionality constant, integral constant and integral order, and hence its design is more complex than that of the usual Integral-order proportional-integral controller. To overcome this complexity in designing, we had used the proposed hybrid algorithm, such that all the design specifications of the motor are satisfied. In order to digitally realize the FO-PI controller, an Oustaloup approximation method has been used. Simulations and comparisons of proposed HDABCA with conventional methods and also other state-of-art methods demonstrate the competence of the proposed approach, especially for actuating fractional order controller for integer order plants.

关键词： PMSM FO-PI speed controller Differential evolution artificial bee colony algorithm

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An efficient and robust artificial bee colony algorithm for numerical optimization

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COMPUTERS & OPERATIONS RESEARCH 2013年第5期40卷 1256-1265页

作者： Xiang, Wan-li An, Mei-qing Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China Tianjin Univ Inst Syst Engn Tianjin 300072 Peoples R China

artificial bee colony (ABC) algorithm has already shown more effective than other population-based algorithms. However, ABC is good at exploration but poor at exploitation, which results in an issue on convergence performance in some cases. To improve the convergence performance of ABC, an efficient and robust artificial bee colony (ERABC) algorithm is proposed. In ERABC, a combinatorial solution search equation is introduced to accelerate the search process. And in order to avoid being trapped in local minima, chaotic search technique is employed on scout bee phase. Meanwhile, to reach a kind of sustainable evolutionary ability, reverse selection based on roulette wheel is applied to keep the population diversity. In addition, to enhance the global convergence, chaotic initialization is used to produce initial population. Finally, experimental results tested on 23 benchmark functions show that ERABC has a very good performance when compared with two ABC-based algorithms. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Initialization based on chaos Reverse selection based on roulette wheel Solution search equation Chaotic search

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An artificial bee colony algorithm for the maximally diverse grouping problem

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INFORMATION SCIENCES 2013年 230卷 183-196页

作者： Rodriguez, Francisco J. Lozano, M. Garcia-Martinez, C. Gonzalez-Barrera, Jonathan D. Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain Univ Cordoba Dept Comp & Numer Anal Cordoba Spain Univ La Laguna Dept Stat Operat Res & Comp Sci San Cristobal Laguna Spain

In this paper, an artificial bee colony algorithm is proposed to solve the maximally diverse grouping problem. This complex optimisation problem consists of forming maximally diverse groups with restricted sizes from a given set of elements. The artificial bee colony algorithm is a new swarm intelligence technique based on the intelligent foraging behaviour of honeybees. The behaviour of this algorithm is determined by two search strategies: an initialisation scheme employed to construct initial solutions and a method for generating neighbouring solutions. More specifically, the proposed approach employs a greedy constructive method to accomplish the initialisation task and also employs different neighbourhood operators inspired by the iterated greedy algorithm. In addition, it incorporates an improvement procedure to enhance the intensification capability. Through an analysis of the experimental results, the highly effective performance of the proposed algorithm is shown in comparison to the current state-of-the-art algorithms which address the problem. (c) 2013 Elsevier Inc. All rights reserved.

关键词： Maximally diverse grouping problem artificial bee colony algorithm Iterated greedy

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Iterative learning control of robot based on artificial bee colony algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2019年第9期233卷 1221-1238页

作者： Xi, Wanqiang Wang, Yaoyao Chen, Bai Wu, Hongtao Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn 29 Yudao St Nanjing 210016 Jiangsu Peoples R China

For the repetitive motion control, inaccurate model, and other issues of industrial robots, this article presents a novel control method that the proportion differentiation-type iterative learning parameters are self-tuning based on artificial bee colony algorithm. Considering the influence of the numerical value of iterative learning parameters on the control system, especially in the early iteration, the control effect is not satisfactory. Thus, the artificial bee colony algorithm is introduced in this article. Using bee colony as search unit, the parameters in iterative learning are optimized through the exchange of information and the survival of fittest between them. And then the optimized results are returned to iterative learning control algorithm. Finally, the digital simulation of a two-degrees-of-freedom manipulator and the experimental verification of a cable-driven robot with its first two joints are carried out. The results show that the iterative learning control based on the artificial bee colony algorithm has faster convergence and better control effect than the iterative learning control with fixed parameters.

关键词： artificial bee colony algorithm iterative learning control robot parameter optimization system simulation

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A novel expression for resonant length obtained by using artificial bee colony algorithm in calculating resonant frequency of C-shaped compact microstrip antennas

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2011年第4期19卷 597-606页

作者： Akdagli, Ali Bicer, Mustafa Berkan Ermis, Seda Mersin Univ Dept Elect & Elect Engn Mersin Turkey Univ Texas Arlington Dept Elect Engn Arlington TX 76019 USA

This paper presents a novel and simple expression for resonant length to calculate the resonant frequency of C-shaped compact microstrip antennas operating on UHF band applications. C-shaped compact microstrip antennas with different physical dimensions and electrical parameters were simulated by means of a software package that employs the method of finite difference time domain. With the aid of the artificial bee colony algorithm, an expression for the resonant length depending on physical dimensions was constructed by using simulation data. The resonant length expression provided less than 1.6% error on average over the simulated 144 antennas. A comparison between the results obtained in this work and previous results presented in the literature is given to show the accuracy of the proposed expression.

关键词： artificial bee colony algorithm compact microstrip antenna microstrip antennas resonant frequency

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Peak factor optimization of multi-harmonic signals using artificial bee colony algorithm

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MEASUREMENT 2020年 150卷 107040-000页

作者： Janeiro, Fernando M. Hu, Yingyue Ramos, Pedro M. Inst Telecomunicacoes Rua Romao Ramalho 59 P-7000671 Evora Portugal Univ Evora Dept Fis Rua Romao Ramalho 59 P-7000671 Evora Portugal Zhongyuan Univ Technol Rd Zhengzhou Shi Henan Sheng Peoples R China Univ Lisbon Inst Super Tecn Dept Engn Elect & Comp Av Rovisco Pais 1 P-1049001 Lisbon Portugal Inst Telecomunicacoes Av Rovisco Pais 1 P-1049001 Lisbon Portugal

Multi-harmonic signals are useful in many applications to estimate the frequency response of a device. To minimize noise effects, signal power should be maximized within the amplitude limitations of the signal generation and acquisition hardware. Signal power depends only on the harmonic amplitudes, while the signal amplitude also depends on the harmonic phases. Careful choice of these phases allows the minimization of the peak factor which corresponds to the minimization of the signal's amplitude for a given signal power. Multiple methods have been proposed to minimize a signal's peak factor, including closed form expressions and iterative algorithms. However, the existence of many local extrema suggests the use of global search algorithms. This paper proposes the use of the artificial bee colony algorithm. The solution is further improved by using a Nelder-Mead Simplex algorithm. The artificial bee colony algorithm results are compared with those of previously published methods. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Multi-harmonic signal Phase selection Peak factor artificial bee colony algorithm

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