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artificial bee colony algorithm and pattern search hybridized for global optimization

APPLIED SOFT COMPUTING 2013年第4期13卷 1781-1791页

作者： Kang, Fei Li, Junjie Li, Haojin Dalian Univ Technol Fac Infrastruct Engn Dalian 116024 Peoples R China

artificial bee colony algorithm is one of the most recently proposed swarm intelligence based optimization algorithm. A memetic algorithm which combines Hooke-Jeeves pattern search with artificial bee colony algorithm is proposed for numerical global optimization. There are two alternative phases of the proposed algorithm: the exploration phase realized by artificial bee colony algorithm and the exploitation phase completed by pattern search. The proposed algorithm was tested on a comprehensive set of benchmark functions, encompassing a wide range of dimensionality. Results show that the new algorithm is promising in terms of convergence speed, solution accuracy and success rate. The performance of artificial bee colony algorithm is much improved by introducing a pattern search method, especially in handling functions having narrow curving valley, functions with high eccentric ellipse and some complex multimodal functions. (C) 2013 Elsevier B. V. All rights reserved.

关键词： artificial bee colony algorithm Swarm intelligence Memetic algorithm Evolutionary computation Global optimization

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artificial bee colony algorithm for clustering: an extreme learning approach

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SOFT COMPUTING 2016年第8期20卷 3163-3176页

作者： Alshamiri, Abobakr Khalil Singh, Alok Surampudi, Bapi Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India Int Inst Informat Technol Cognit Sci Lab Hyderabad 500032 Andhra Pradesh India

Extreme learning machine (ELM) as a new learning approach has shown its good generalization performance in regression and classification applications. Clustering analysis is an important tool to explore the structure of data and has been employed in many disciplines and applications. In this paper, we present a method that builds on ELM projection of input data into a high-dimensional feature space and followed by unsupervised clustering using artificial bee colony (ABC) algorithm. While ELM projection facilitates separability of clusters, a metaheuristic technique such as ABC algorithm overcomes problems of dependence on initialization of cluster centers and convergence to local minima suffered by conventional algorithms such as K-means. The proposed ELM-ABC algorithm is tested on 12 benchmark data sets. The experimental results show that the ELM-ABC algorithm can effectively improve the quality of clustering.

关键词： Clustering Extreme learning machine K-means algorithm artificial bee colony algorithm

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The Bisection-artificial bee colony algorithm to solve Fixed point problems

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APPLIED SOFT COMPUTING 2015年 26卷 143-148页

作者： Mansouri, P. Asady, B. Gupta, N. Islamic Azad Univ Dept Math Arak Branch Arak Iran Univ Delhi Dept Comp Sci Delhi 110007 India

In this paper, we introduce a novel iterative method to finding the fixed point of a nonlinear function. Therefore, we combine ideas proposed in artificial bee colony algorithm (Karaboga and Basturk, 2007) and Bisection method (Burden and Douglas, 1985). This method is new and very efficient for solving a nonlinear equation. We illustrate this method with four benchmark functions and compare results with others methods, such as ABC, PSO, GA and Firefly algorithms. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Bisection method Fixed point problems artificial bee colony algorithm

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artificial bee colony algorithm for small signal model parameter extraction of MESFET

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ENGINEERING APPLICATIONS OF artificial INTELLIGENCE 2010年第5期23卷 689-694页

作者： Sabat, Samrat L. Udgata, Siba K. Abraham, Ajith Univ Hyderabad Sch Phys Hyderabad 500046 Andhra Pradesh India Univ Hyderabad Dept Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India Sci Network Innovat & Res Excellence Machine Intelligence Res Labs Auburn WA 98071 USA

This paper presents an application of swarm intelligence technique namely artificial bee colony (ABC) to extract the small signal equivalent circuit model parameters of GaAs metal extended semiconductor field effect transistor (MESFET) device and compares its performance with particle swarm optimization (PSO) algorithm. Parameter extraction in MESFET process involves minimizing the error, which is measured as the difference between modeled and measured S parameter over a broad frequency range. This error surface is viewed as a multi-modal error surface and robust optimization algorithms are required to solve this kind of problem. This paper proposes an ABC algorithm that simulates the foraging behavior of honey bee swarm for model parameter extraction. The performance comparison of both the algorithms (ABC and PSO) are compared with respect to computational time and the quality of solutions (QoS). The simulation results illustrate that these techniques extract accurately the 16-element small signal model parameters of MESFET. The efficiency of this approach is demonstrated by a good fit between the measured and modeled S-parameter data over a frequency range of 0.5-25 GHz. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Particle swarm optimization Parameter extraction MESFET small signal model Swarm intelligence

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artificial bee colony algorithm to design two-channel quadrature mirror filter banks

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SWARM AND EVOLUTIONARY COMPUTATION 2015年 21卷 24-31页

作者： Agrawal, S. K. Sahu, O. P. Natl Inst Technol Elect & Commun Engn Dept Kurukshetra 136119 Haryana India

artificial bee colony (ABC) algorithm has been introduced recently for solving optimization problems. The ABC algorithm is based on intelligent foraging behavior of honeybee swarms and has many advantages over earlier swarm intelligence algorithms. In this work, a new method based on ABC algorithm for designing two-channel quadrature mirror filter (QMF) banks with linear phase is presented. To satisfy the perfect reconstruction condition, low-pass prototype filter coefficients are optimized to minimize an objective function. The objective function is formulated as a weighted sum of four terms, pass-band error, and stop-band residual energy of low-pass analysis filter, square error of the overall transfer function at the quadrature frequency and amplitude distortion of the QMF bank. The design results of the proposed method are compared with earlier reported results of particle swarm optimization (PSO), differential-evolution (DE) and conventional optimization algorithms. (C) 2014 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Perfect reconstruction Swarm intelligence Multirate filter bank

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artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2021年第6期20卷 2530-2562页

作者： Tefek, Mehmet Fatih Osmaniye Korkut Ata Univ Dept Comp Engn Osmaniye Turkey

In this study, an ABC-Local Search (ABC-Ls) method was proposed by including a new local search procedure into the standard artificial bee colony (ABC) algorithm to perform the parameter estimation of photovoltaic systems (PV). The aim of the proposed ABC-Ls method was to improve the exploration capability of the standard ABC with a new local search procedure in addition to the exploitation and exploration balance of the standard ABC algorithm. The proposed ABC-Ls method was first tested on 15 well-known benchmark functions in the literature. In the results of the Friedman Mean Rank test used for statistical analysis, ABC-Ls method successfully ranked first with a value of 1.300 in benchmark functions. After obtaining successful results on the benchmark tests, the proposed ABC-Ls method was applied to the single diode, double diode and Photowatt-PWP-201 PV modules of PV systems for parameter estimations. In addition, the proposed ABC-Ls method has been applied to the KC200GT PV module for parameter estimation under different temperature and irradiance conditions of the PV modules. The success of ABC-Ls method was compared with genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ABC algorithm, tree seed algorithm (TSA), Jaya, Atom search optimization (ASO). The comparison results were presented in tables and graphics in detail. The RMSE values for the parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module of the proposed ABC-LS method were found as 9.8602E-04, 9.8257E-04 and 2.4251E-03, respectively. In this context, the proposed ABC-LS method has been compared with the literature for parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module and it has been found that it provides a parameter estimation similar or better than other studies. The proposed ABC-Ls method for parameter estimation of the KC200GT PV module under different conditions is shown in convergence graphs and box plots, where it ac

关键词： artificial bee colony algorithm Local search Photovoltaic systems Photovoltaic module parameter estimation

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artificial bee colony algorithm Based on Dimensional Memory Mechanism and Adaptive Elite Population for Training artificial Neural Networks

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IEEE ACCESS 2023年 11卷 107616-107637页

作者： Zhang, Yiyang Pang, Bao Song, Yong Xu, Qingyang Yuan, Xianfeng Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China

Based on dimensional memory mechanism and adaptive elite population, this paper proposes a satisfactory and efficient artificial bee colony algorithm (DMABC_elite) to solve optimization problems and train artificial neural networks (ANN). DMABC_elite proposes the concept of adaptive elite population that changes dynamically with the search process, and modifies the search equations for employed and onlooker bee phases on this basis. In addition, a dimensional memory mechanism has been introduced that allows multi-dimensional updates, which improves exploitation and speeds up convergence. Next, a new selection strategy and a Levy flight-based solution-generating method are introduced in the scout bee phase to enhance the global search ability. Finally, the performance of DMABC_elite on two different problem groups is analyzed experimentally. On the one hand, DMABC_elite is evaluated using 22 classical benchmark functions with different dimensions and CEC 2013 test functions. Compared with basic ABC and nine state-of-the-art ABC variants, DMABC_elite achieved better results, ranking first in all 10-, 30- and 100-dimensional tests across 22 classical benchmark functions and 30-dimensional tests across CEC 2013 test functions. On the other hand, DMABC_elite is compared with traditional backpropagation-based algorithms and other ABC variants when training seven different ANNs. The results show that DMABC_elite is efficient and competitive in training ANNs.

关键词： artificial bee colony algorithm Search problems adaptive elite population dimensional memory mechanism search equation artificial neural network

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artificial bee colony algorithm-optimized error minimized extreme learning machine and its application in short-term wind speed prediction

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WIND ENGINEERING 2019年第3期43卷 263-276页

作者： Tian, Zhongda Wang, Gang Li, Shujiang Wang, Yanhong Wang, Xiangdong Shenyang Univ Technol Coll Informat Sci & Engn Shenyang 110870 Liaoning Peoples R China

In order to improve the prediction accuracy of short-term wind speed, a short-term wind speed prediction model based on artificial bee colony algorithm optimized error minimized extreme learning machine model is proposed. The extreme learning machine has the advantages of fast learning speed and strong generalization ability. But many useless neurons of incremental extreme learning machine have little influences on the final output and, at the same time, reduce the efficiency of the algorithm. The optimal parameters of the hidden layer nodes will make network output error of incremental extreme learning machine decrease with fast speed. Based on the error minimized extreme learning machine, artificial bee colony algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons, reduce training and prediction error, achieve the goal of reducing the network complexity, and improve the efficiency of the algorithm. The error minimized extreme learning machine prediction model is constructed with the obtained optimal parameters. The stability and convergence property of artificial bee colony algorithm optimized error minimized extreme learning machine model are proved. The practical short-term wind speed time series is used as the research object and to verify the validity of the prediction model. Multi-step prediction simulation of short-term wind speed is carried out. Compared with other prediction models, simulation results show that the prediction model proposed in this article reduces the training time of the prediction model and decreases the number of hidden layer nodes. The prediction model has higher prediction accuracy and reliability performance, meanwhile improves the performance indicators.

关键词： Extreme learning machine artificial bee colony algorithm short-term wind speed prediction optimization

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artificial bee colony algorithm for Real Estate Portfolio Optimization Based on Risk Preference Coefficient

Artificial Bee Colony Algorithm for Real Estate Portfolio Op...

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17th International Conference on Management Science and Engineering (ICMSE)

作者： Liu Hong-mei Wang Zhuo-fu Li Hui-min Hohai Univ Sch Business Nanjing 210098 Peoples R China

ISBN: (纸本)9781424481194;9781424481170

Optimization of real estate portfolio is to select two or more different types of real estate for investment, and the previous models based on expected return-variance cannot meet the needs of the investors. Furthermore, the investors change their risk preference with the risk level. In this study, firstly, the semi-variance model of real estate investment portfolio based on risk preference coefficient was constructed. The return per unit of risk is the key factor to determine an investment decision. Secondly, artificial bee colony algorithm (ABC) was employed to solve the constructed model. Finally, a real-world case was analyzed to verify the performance of ABC. The result indicated that ABC could generate better solution than GA and it could be regarded as a useful approach for solving real estate portfolio problem.

关键词： real estate portfolio combinatorial optimization artificial bee colony algorithm risk preference coefficient semi-variance

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artificial bee colony algorithm for Two-dimensional Loading Capacitated Vehicle Routing Problem

Artificial Bee Colony Algorithm for Two-dimensional Loading ...

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20th International Annual Conference on Management Science and Engineering

作者： Wu Bin Cai Hong Cui Zhi-yong Nanjing Univ Technol Sch Econ & Management Nanjing 210009 Jiangsu Peoples R China

ISBN: (纸本)9781479904716

The artificial bee colony algorithm (ABC) with three loading heuristics for the two-dimensional loading capacitated vehicle routing problem (2L-CVRP) is presented in the paper. The 2L-CVRP is a combination of two well-known NP-hard problems, the capacitated vehicle routing problem, and the two-dimensional bin packing problem. It is very difficult to get a good performance solution in practice for these problems. The problem is solved by different heuristics for the loading part, and by artificial bee colony algorithm for the overall optimization. To solve the representation problem of the solution, a novel real encoding is presented to represent the solution for ABC. The effectiveness of the proposed algorithm is tested, and proven by extensive computational experiments on benchmark instances.

关键词： artificial bee colony algorithm 2L-CVRP bin packing problem vehicle routing problem

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