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artificial bee colony algorithm Combined With Previous Successful Search Experience

IEEE ACCESS 2019年 7卷 34318-34332页

作者： Liu, Kui Wang, Chunfeng Liu, Sanyang Henan Normal Univ Coll Math & Informat Sci Xinxiang 453007 Peoples R China Henan Normal Univ Xinxiang 453007 Peoples R China Xidian Univ Dept Appl Math Xian 710071 Shaanxi Peoples R China

The neighboring solutions-based technique employed by the artificial bee colony algorithm (ABC) is good at exploration but poor at exploitation. The main reason is the blindness of search behavior which leads to the employed bees not generating promising candidate solutions. To address this issue, we propose an improved ABC algorithm (ABCPSE) combined with the previous successful search experience. The proposed algorithm has the following innovative advantages: First, the previous successful search experience with good performances in convergence and distribution are employed in real time to generate offspring. This rule can increase convergence speed and exploitation ability of ABCPSE algorithm. Second, local search is performed near the superior individuals produced in a different generation. Hence, a set of solutions with excellent diversity and convergence is obtained. To assess the performance of ABCPSE algorithm, experiments are conducted on a set of 18 benchmark functions. The results demonstrate that the proposed algorithm can produce higher quality solutions with faster convergence than some current stateof- the-art ABC-based algorithms.

关键词： artificial bee colony algorithm Successful search experience experience matrix solution search equation

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artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2015年 429卷 125-139页

作者： Chen, Wei Capital Univ Econ & Business Sch Informat Beijing 100070 Peoples R China

In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Fuzzy portfolio optimization Semiabsolute deviation Cardinality constraint artificial bee colony algorithm Heuristics

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artificial bee colony algorithm with scanning strategy for the periodic vehicle routing problem

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2013年第6期89卷 762-770页

作者： Yao, Baozhen Hu, Ping Zhang, Mingheng Wang, Shuang Dalian Univ Technol Sch Automot Engn Dalian 116024 Peoples R China

The periodic vehicle routing problem (PVRP) is an extension of the vehicle routing problem (VRP). Because it extends the single delivery period to a T-day period (T > 1), PVRP has strong theoretical and practical significance. Since PVRP is an embedded VRP, it is more complex and difficult compared with the general VRP. In this paper, the bee colony algorithm is used to solve the PVRP. To improve the performance of this algorithm, multidimensional heuristic information and a local optimization based on a scanning strategy are used. At the end of this paper, the algorithm is tested by some well-known examples. The results show that the proposed improved bee colony algorithm is a powerful tool for solving the PVRP. It also shows that these two kinds of strategies can significantly improve the performance of the algorithm.

关键词： Periodic vehicle routing problem artificial bee colony algorithm multidimensional heuristic information scanning strategy

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artificial bee colony algorithm-based design of discrete-time stable unknown input estimator

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INFORMATION SCIENCES 2023年第1期634卷 621-649页

作者： Satoh, Toshiyuki Nishizawa, Shun Nagase, Jun-ya Saito, Naoki Saga, Norihiko Akita Prefectural Univ Dept Intelligent Mechatron 84-4 Aza-Ebinokuchi Yurihonjo Akita 0150055 Japan Ryukoku Univ Fac Adv Sci & Technol 1-5 YokotaniSeta Oe Cho Otsu Shiga 5202194 Japan Kwansei Gakuin Univ Dept Engn Artificial Intelligence & Mech Engn Cour 1 Gakuen Uegahara Sanda Hyogo 6691330 Japan

This paper addresses the design problem of the discrete-time stable unknown input estimator (UIE) based on parameter optimization using the artificial bee colony (ABC) algorithm. First, a stability-guaranteed design method for UIEs is presented, and a sufficient condition for the applicability of the design is provided. Next, to design UIEs with good disturbance rejection properties, a new objective function is developed that incorporates both waveform-based and norm-based performance criteria to allow direct evaluation of the adverse effects of disturbances on system performance. Finally, the proposed design method is compared with the previous one using an objective function based on the estimated disturbance to confirm the improvement of the disturbance rejection properties at the plant output. Furthermore, as another approach to the design of stable UIEs, the original UIE design method is combined with a constrained ABC algorithm, and the approach is compared with the proposed one in terms of disturbance rejection properties.

关键词： Unknown input estimator Disturbance rejection artificial bee colony algorithm Parameter optimization

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artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 54卷 144-153页

作者： Aydin, Dogan Ozyon, Serdar Yasar, Celal Liao, Tianjun Dumlupinar Univ Dept Comp Engn TR-43100 Kutahya Turkey Dumlupinar Univ Dept Elect & Elect Engn TR-43100 Kutahya Turkey Univ Libre Bruxelles IRIDIA CoDE Brussels Belgium

Incremental artificial bee colony algorithm with Local Search (IABC-LS) is one of efficient variant of artificial bee colony optimization which was successfully applied to economic power dispatch problems before. However IABC-LS algorithm has some tunable parameters which are directly affecting the algorithm behavior. In this study, we have introduced a new algorithm namely artificial bee colony with Dynamic Population size (ABCDP) which is using similar mechanisms defined in IABC-LS without using many parameters to be tuned. To prove the efficiency and robustness of algorithm in power dispatch, the algorithm is used for the combined economic and emission dispatch problem which is converted into single objective optimization problem. For fair comparison, the parameters of both IABC and ABCDP algorithms are determined via automatic parameter configuration tool, Iterated F-Race. IEEE 30 bus test system and 40-generator units problem are used as the problem instances. The results of the algorithms indicate that ABCDP is giving good results in both systems and very competitive with the state-of-the-art. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Combined economic and emission dispatch Weighted sum method artificial bee colony algorithm Dynamic population size Parameter control

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artificial bee colony algorithm with Principal Component Analysis

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ELECTRONICS AND COMMUNICATIONS IN JAPAN 2016年第9期99卷 47-61页

作者： Mori, Daisuke Yamaguchi, Satoshi Chiba Inst Technol Grad Sch Informat & Comp Sci Narashino Chiba Japan Chiba Inst Technol Fac Informat & Comp Sci Narashino Chiba Japan

This paper proposes novel artificial bee colony (ABC) algorithms for solving problems including interdependence among variables. ABC algorithms are one method of solving multivariable real number space optimization problems, in which the search space is a set of vectors constructed of variables. The main search process in the ordinary ABC algorithm creates a new solution vector by changing only one variable of the current solution vector. Therefore, the new solution vector is created along only one coordinate axis. This procedure, however, is not appropriate for solving problems including interdependence among variables. For such problems, a method that is able to change more than one variable of a solution vector at the same time is required. In our proposed methods, the original coordinate axes are transformed to linearly uncorrelated axes by using principal component analysis (PCA) in the searching process. Our ABC algorithms create a new solution vector along one of the axes transformed by PCA. Hence, from the viewpoint of the original coordinate axes, the new algorithms are able to change more than one variable. The proposed algorithms have been compared with the ordinary ABC algorithm by solving five benchmark problems. Through the computer simulation results, our algorithms were shown to have better performance for solving problems including interdependence among variables than the ordinary ABC algorithm.

关键词： artificial bee colony algorithm principal component analysis interdependence among variables medial axis transformation

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artificial bee colony algorithm and pattern search hybridized for global optimization

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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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artificial bee colony algorithm based on Dominance (ABCD) for a hybrid gene selection method

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KNOWLEDGE-BASED SYSTEMS 2020年 205卷 106323-106323页

作者： Coleto-Alcudia, Veredas Vega-Rodriguez, Miguel A. Univ Extremadura Dept Comp & Commun Technol Campus Univ S-N Caceres 10003 Spain

In cancer research, it is important to classify tissue samples in different classes (normal, tumour, tumour type, etc.). Gene selection purpose is to find the minimum number of genes that can predict sample classes with efficacy. This work is focused on the gene selection problem by introducing a new hybrid method. This new method combines a first step of gene filtering with an optimization algorithm in a second step to find the best subset of genes for the classification task. The first step uses the Analytic Hierarchy Process, in which five ranking methods are used to select the most relevant genes in the dataset. In this way, this gene filtering reduces the number of genes to manage. Regarding the second step, the gene selection can be divided into two objectives: minimizing the number of selected genes and maximizing the classification accuracy. Therefore, we have used a multi-objective optimization approach. More exactly, an artificial bee colony based on Dominance (ABCD) algorithm has been proposed for this second step. Our approach has been tested with eleven real cancer datasets and the results have been compared with several multi-objective methods proposed in the scientific literature. Our results show a high accuracy in the classification task with a small subset of genes. Also, to prove the relevance of our proposal, a biological analysis has been developed on the genes selected. The conclusions of this biological analysis are positive, because the selected genes are closely linked to the cancer dataset they belong to. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Gene selection Multi-objective optimization artificial bee colony algorithm Cancer Support vector machine Analytic hierarchy process

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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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