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Wind speed prediction using the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 73卷 625-631页

作者： Fei, Sheng-wei He, Yong Donghua Univ Sch Mech Engn Shanghai 201620 Peoples R China

In this paper, the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine (WABCRVM) is presented for wind speed prediction. Here, wind speed can be regarded as a signal and decomposed into four decomposed signals with different frequency range, which can be obtained by 2-layer wavelet decomposition for wind speed data, and the prediction models of the four decomposed signals can be established by RVM with their each appropriate embedding dimension. artificial bee colony algorithm (ABC) is used to select the appropriate kernel parameters of their RVM models. Thus, each decomposed signal's RVM model of wind speed has appropriate embedding dimension and kernel parameter. Finally, the experimental results show that it is feasible for the proposed combination scheme to improve the prediction ability of RVM for wind speed. (C) 2015 Published by Elsevier Ltd.

关键词： Speed wind prediction Wavelet decomposition Embedding dimension artificial bee colony algorithm

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An adaptive artificial bee colony algorithm for global optimization

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APPLIED MATHEMATICS AND COMPUTATION 2015年 271卷 1004-1023页

作者： Yurtkuran, Alkin Emel, Erdal Uludag Univ Dept Ind Engn TR-16059 Bursa Turkey

artificial bee colony algorithm (ABC) is a recently introduced swarm based meta heuristic algorithm. ABC mimics the foraging behavior of honey bee swarms. Original ABC algorithm is known to have a poor exploitation performance. To remedy this problem, this paper proposes an adaptive artificial bee colony algorithm (AABC), which employs six different search rules that have been successfully used in the literature. Therefore, the AABC benefits from the use of different search and information sharing techniques within an overall search process. A probabilistic selection is applied to deterinine the search rule to be used in generating a candidate solution. The probability of selecting a given search rule is further updated according to its prior performance using the roulette wheel technique. Moreover, a ineinoly length is introduced corresponding to the maximum number of moves to reset selection probabilities. Experiments are conducted using well-known benchmark problems with varying dimensionality to compare AABC with other efficient ABC variants. Computational results reveal that the proposed AABC outperforms other novel ABC variants. (C) 2015 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Adaptive search Global optimization

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A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems

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BIOSYSTEMS 2015年 132卷 43-53页

作者： Shan, Hai Yasuda, Toshiyuki Ohkura, Kazuhiro Hiroshima Univ Grad Sch Engn Higashihiroshima 739852 Japan

The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively. (C) 2015 Elsevier Ireland Ltd. All rights reserved.

关键词： artificial bee colony algorithm Swarm intelligence Continuous optimization problems Self adaptive mechanism CEC 2014 test suite

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Optimization of Spherical Roller Bearing Design Using artificial bee colony algorithm and Grid Search Method

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INTERNATIONAL JOURNAL FOR COMPUTATIONAL METHODS IN ENGINEERING SCIENCE & MECHANICS 2015年第4期16卷 221-233页

作者： Tiwari, Rajiv Waghole, Vikas Indian Inst Technol Guwahati Dept Mech Engn Gauhati 781039 Assam India

Bearing standards impose restrictions on the internal geometry of spherical roller bearings. Geometrical and strength constraints conditions have been formulated for the optimization of bearing design. The long fatigue life is one of the most important criteria in the optimum design of bearing. The life is directly proportional to the dynamic capacity;hence, the objective function has been chosen as the maximization of dynamic capacity. The effect of speed and static loads acting on the bearing are also taken into account. Design variables for the bearing include five geometrical parameters: the roller diameter, the roller length, the bearing pitch diameter, the number of rollers, and the contact angle. There are a few design constraint parameters which are also included in the optimization, the bounds of which are obtained by initial runs of the optimization. The optimization program is made to run for different values of these design constraint parameters and a range of the parameters is obtained for which the objective function has a higher value. The artificial bee colony algorithm (ABCA) has been used to solve the constrained optimized problem and the optimum design is compared with the one obtained from the grid search method (GSM), both operating independently. Both the ABCA and the GSM have been finally combined together to reach the global optimum point. A constraint violation study has also been carried out to give priority to the constraint having greater possibility of violations. Optimized bearing designs show a better performance parameter with those specified in bearing catalogs. The sensitivity analysis of bearing parameters has also been carried out to see the effect of manufacturing tolerance on the objective function.

关键词： Spherical Roller Bearings Optimum Design artificial bee colony algorithm Grid Search Method Sensitivity Analysis Constraint Violation

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Implementation of Parallel Multi-objective artificial bee colony algorithm Based on Spark Platform

Implementation of Parallel Multi-objective Artificial Bee Co...

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International Conference on Computer Science and Education

作者： Chunfeng Li Tingxi Wen Huailin Dong Qingfeng Wu Zhongnan Zhang Software School Xiamen University Xiamen China

ISBN: (纸本)9781509022199

The highly parallel framework of Spark enables it to have outstanding advantages in accelerating computing speed. At the same time, artificial bee colony (ABC) algorithm needs to improve its performance because of its high time complexity. In this paper, we combine ABC algorithm with Spark platform. First, we extend ABC algorithm to the multi-objective artificial bee colony (MOABC) algorithm;then we implement the parallel MOABC algorithm based on Spark platform. We conduct several experiments using our proposed algorithm on Spark platform, and the experimental results show that parallel MOABC algorithm produces a better performance.

关键词： Parallel Computing artificial bee colony algorithm Multi-Objective Spark Platform MapReduce

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Dynamic Topology Optimization for Multiple Eigenfrequencies using the artificial bee colony algorithm

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2015年第8期16卷 1817-1824页

作者： Chang, Dae-Ho Han, Seog-Young Hanyang Univ Grad Sch Dept Mech Engn Seoul 133791 South Korea Hanyang Univ Div Mech Engn Seoul 133791 South Korea

The purpose of this study is to suggest a method of applying the artificial bee colony algorithm (ABCA) in the frequency topology optimization for a structure with multiple eigenfrequencies. In order to replicate the multiple eigenfrequencies of a structure, sub-optimization procedure for multiple eigenfrequencies was additionally developed. In order to obtain a stable and robust optimal topology the waggle index update rule, evaluation method of fitness values and changing filtering size scheme were also employed And the optimized topologies of ABCA for examples were compared with those of the solid isotropic material with penalization (SIMP) method for investigating the applicability and effectiveness of the ABCA. The following conclusions were obtained through the results of examples;(I) The ABCA implemented with sub-optimization procedure and the three suggested schemes, is very applicable and effective in dynamic topology optimization. (2) The multiple eigenfrequencies of a structure are successfully replicated by the ABCA in optimization procedure. (3) The fundamental frequency of the ABCA is almost the same or slightly higher than that of the SIMP.

关键词： artificial bee colony algorithm Finite element method Multiple eigenfrequencies Stochastic search method Topology optimization

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artificial bee colony algorithm Based on Information Learning

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IEEE TRANSACTIONS ON CYBERNETICS 2015年第12期45卷 2827-2839页

作者： Gao, Wei-Feng Huang, Ling-Ling Liu, San-Yang Dai, Cai China Univ Petr Qingdao 266580 Peoples R China Xidian Univ Sch Sci Xian 710071 Peoples R China Shaanxi Normal Univ Coll Comp Sci Xian 710062 Peoples R China

Inspired by the fact that the division of labor and cooperation play extremely important roles in the human history development, this paper develops a novel artificial bee colony algorithm based on information learning (ILABC, for short). In ILABC, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of subpopulation is dynamically adjusted based on the last search experience, which results in a clear division of labor. Furthermore, the two search mechanisms are designed to facilitate the exchange of information in each subpopulation and between different subpopulations, respectively, which acts as the cooperation. Finally, the comparison results on a number of benchmark functions demonstrate that the proposed method performs competitively and effectively when compared to the selected state-of-the-art algorithms.

关键词： artificial bee colony algorithm clustering partition search equation search mechanism

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Reduction of artificial bee colony algorithm for global optimization

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NEUROCOMPUTING 2015年 148卷 70-74页

作者： Maeda, Michiharu Tsuda, Shinya Fukuoka Inst Technol Higashi Ku Fukuoka Japan

This paper presents a reduction of artificial bee colony algorithm for global optimization. artificial bee colony algorithm is an optimization technique which refers to the behavior of honeybee swarms, and a multi-point search approach which finds a best solution using multiple bees. For avoiding local minima, a number of bees are initially prepared and their positions are updated by artificial bee colony algorithm. bees sequentially reduce to reach a predetermined number of them grounded in the evaluation value and artificial bee colony algorithm continues until the termination condition is met. In order to show the effectiveness of the proposed algorithm, we examine the best value by using test functions compared to existing algorithms. Furthermore the influence of best value on the initial number of bees for our algorithm is discussed. (C) 2014 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Global optimization Metaheuristics Reduction

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Improved clustering criterion for image clustering with artificial bee colony algorithm

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PATTERN ANALYSIS AND APPLICATIONS 2015年第3期18卷 587-599页

作者： Ozturk, Celal Hancer, Emrah Karaboga, Dervis Erciyes Univ Fac Engn Dept Comp Engn Kayseri Turkey

In this paper, a new objective function is proposed for image clustering and is applied with the artificial bee colony (ABC) algorithm, the particle swarm optimization algorithm and the genetic algorithm. The performance of the proposed objective function is tested on seven benchmark images by comparing it with the three well-known objective functions in the literature and the K-means algorithm in terms of separateness and compactness which are the main criterions of the clustering problem. Moreover, the Davies-Bouldin Index and the XB Index are also employed to compare the quality of the proposed objective function with the other objective functions. The simulated results show that the ABC-based image clustering method with the improved objective function obtains well-distinguished clusters.

关键词： Image clustering artificial bee colony algorithm Genetic algorithms K-means Particle swarm optimization Validity indexes

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A Hybrid Nature-Inspired artificial bee colony algorithm for Uncapacitated Examination Timetabling Problems

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JOURNAL OF INTELLIGENT SYSTEMS 2015年第1期24卷 37-54页

作者： Bolaji, Asaju Khader, Ahamad Al-Betar, Mohammed Awadallah, Mohammed Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Univ Ilorin Dept Comp Sci Ilorin Nigeria Univ Sains Malaysia Sch Comp Sci George Town Malaysia Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid Jordan

This article presents a Hybrid artificial bee colony (HABC) for uncapacitated examination timetabling. The ABC algorithm is a recent metaheuristic population-based algorithm that belongs to the Swarm Intelligence technique. Examination timetabling is a hard combinatorial optimization problem of assigning examinations to timeslots based on the given hard and soft constraints. The proposed hybridization comes in two phases: the first phase hybridized a simple local search technique as a local refinement process within the employed bee operator of the original ABC, while the second phase involves the replacement of the scout bee operator with the random consideration concept of harmony search algorithm. The former is to empower the exploitation capability of ABC, whereas the latter is used to control the diversity of the solution search space. The HABC is evaluated using a benchmark dataset defined by Carter, including 12 problem instances. The results show that the HABC is better than exiting ABC techniques and competes well with other techniques from the literature.

关键词： artificial bee colony algorithm Swarm Intelligence metaheuristics timetabling problem examination timetabling problem

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