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An adaptive artificial bee colony algorithm for global optimization

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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Application of an improved artificial bee colony algorithm to inverse problem of aerosol optical constants from spectral measurement data

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OPTIK 2017年 145卷 316-329页

作者： He, Zhenzong Mao, Junkui Han, Xingsi Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Jiangsu Peoples R China

An improved artificial bee colony (IABC) algorithm is introduced to obtain the optical constants of aerosols from the spectral measurement data of the aerosol dispersion medium. The direct problem solved using the Finite Volume Method (FVM) combined with Mie scattering theory is used to study the radiative transfer process in the aerosol dispersion medium. Compared with standard artificial bee colony (ABC) algorithm, the IABC can avoid local optima and improve convergence accuracy. Based on the IABC, the optical constants of aerosols over Harbin, China are retrieved under different random measurement errors. Results indicate that there is acceptable retrieval accuracy without errors, and the retrieval accuracy reduces if errors increase. To improve retrieval accuracy, two improved inverse models, namely as double-concentration and double-layer inverse models, are proposed. The investigation reveals that the improved inverse models, especially double-concentration inverse model, can give more accurate predictions even with errors. Finally, the optical constants of aerosols over Beijing available on the website of AERONET are also reconstructed by IABC satisfactorily. All the results show that the IABC is a potential and effective optimal algorithm to retrieve the optical constants of aerosols, especially under improved inverse models. (C) 2017 Published by Elsevier GmbH.

关键词： artificial bee colony algorithm Aerosol Optical constant Inverse radiative problem

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Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 137卷 46-58页

作者： Zhang, Yong Cheng, Shi Shi, Yuhui Gong, Dun-Wei Zhao, Xinchao China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Shaanxi Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing Peoples R China

Since different features may require different costs, the cost-sensitive feature selection problem become more and more important in real-world applications. Generally, it includes two main conflicting objectives, i.e., maximizing the classification performance and minimizing the feature cost. However, most existing approaches treat this task as a single-objective optimization problem. To satisfy various requirements of decision-makers, this paper studies a multi-objective feature selection approach, called two-archive multi-objective artificial bee colony algorithm (TMABC-FS). Two new operators, i.e., convergence-guiding search for employed bees and diversity-guiding search for onlooker bees, are proposed for obtaining a group of non-dominated feature subsets with good distribution and convergence. And two archives, i.e., the leader archive and the external archive are employed to enhance the search capability of different kinds of bees. The proposed TMABC-FS is validated on several datasets from UCI, and is compared with two traditional algorithms and three multi-objective methods. Results have shown that TMABC-FS is an efficient and robust optimization method for solving cost-sensitive feature selection problems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Cost-sensitive feature selection artificial bee colony algorithm Multi-objective optimization Particle swarm optimization Differential evolution

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Application of discrete artificial bee colony algorithm for cloud task optimization scheduling

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International Journal of Modeling, Simulation, and Scientific Computing 2020年第4期11卷 190-200页

作者： Shuai Man Rongjie Yang Langfang Health Vocational College Langfang Hebei 065000P.R.China

The performance of task scheduling algorithm in cloud computing determines the performance of the cloud *** study mainly analyzed the application of the artificial bee colony(ABC)algorithm in the cloud task *** order to solve the problem of cloud task scheduling,the ABC algorithm was discretized to get the discrete artificial bee colony(DABC)*** the mathematical model of cloud task scheduling was established and solved by the DABC ***,the simulation experiment was carried out,and the performance of first-come-first-served(FCFS),MIN–MIN,ABC and DABC algorithms under different cloud tasks was compared to verify the performance of the proposed *** results showed that the user waiting time of the DABC algorithm was 1210s,the load balance degree was 0.01,and the user payment fee was 1688 yuan when the number of cloud tasks was 500;compared with other algorithms,the user waiting time of the DABC algorithm was shorter,the resource load balance degree was higher,and the overall performance was *** research results verify the effectiveness of the DABC algorithm in solving the problem of cloud task optimal scheduling,and it can be further extended and applied in practice.

关键词： Cloud task scheduling artificial bee colony algorithm optimization method cloud computing.

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Comparison of Improved Sheep Flock Heredity algorithm and artificial bee colony algorithm for lot Streaming in m-Machine Flow Shop Scheduling

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014年第5期39卷 4285-4300页

作者： Vijaychakaravarthy, G. Marimuthu, S. Sait, A. Naveen Vetri Vinayaha Coll Engn & Technol Tiruchirappalli 621215 India Latha Mathavan Engn Coll Madurai 625301 Tamil Nadu India Chenduran Coll Engn & Technol Pudukkottai 622507 India

Lot streaming is a technique used to split the processing of lots into several sublots (transfer batches) to allow the overlapping of operations in a multistage manufacturing systems thereby shortening the production time (makespan). The objective of this paper is to minimize the makespan and total flow time of n-job, m-machine lot-streaming problem in a flow shop with variable size sublots and also to determine the optimal sublot size. In recent times researchers are concentrating and applying intelligent heuristics to solve Flow shop problems with lot streaming. In this research, Improved Sheep Flock Heredity algorithm (ISFHA) and artificial bee colony (ABC) algorithms are used to solve the problem. The results obtained by the proposed algorithms are also compared with the performance of other worked out traditional heuristics. The computational results show that the identified algorithms are more efficient, effective and better than the algorithms already tested for this problem.

关键词： Flow shop Lot streaming Scheduling Improved Sheep Flock Heredity algorithm artificial bee colony algorithm

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Variable selection in gamma regression models via artificial bee colony algorithm

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JOURNAL OF APPLIED STATISTICS 2018年第1期45卷 8-16页

作者： Dunder, Emre Gumustekin, Serpil Cengiz, Mehmet Ali Univ Ondokuz Mayis Dept Stat Samsun Turkey

Variable selection is an important task in regression analysis. Performance of the statistical model highly depends on the determination of the subset of predictors. There are several methods to select most relevant variables to construct a good model. However in practice, the dependent variable may have positive continuous values and not normally distributed. In such situations, gamma distribution is more suitable than normal for building a regression model. This paper introduces an heuristic approach to perform variable selection using artificial bee colony optimization for gamma regression models. We evaluated the proposed method against with classical selection methods such as backward and stepwise. Both simulation studies and real data set examples proved the accuracy of our selection procedure.

关键词： Variable selection gamma regression analysis heuristic optimization artificial bee colony algorithm R-project

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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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Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem

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COMPLEX & INTELLIGENT SYSTEMS 2021年第3期7卷 1527-1545页

作者： Ebrahimnejad, Ali Enayattabr, Mohammad Motameni, Homayun Garg, Harish Islamic Azad Univ Qaemshahr Branch Dept Math Qaemshahr Iran Islamic Azad Univ Babol Branch Dept Comp Engn Sari Iran Islamic Azad Univ Sari Branch Dept Comp Engn Sari Iran Thapar Inst Engn & Technol Sch Math Patiala 147004 Punjab India

In recent years, numerous researchers examined and analyzed several different types of uncertainty in shortest path (SP) problems. However, those SP problems in which the costs of arcs are expressed in terms of mixed interval-valued fuzzy numbers are less addressed. Here, for solving such uncertain SP problems, first a new procedure is extended to approximate the summation of mixed interval-valued fuzzy numbers using alpha cuts. Then, an extended distance function is introduced for comparing the path weights. Finally, we intend to use a modified artificial bee colony (MABC) algorithm to find the interval-valued membership function of SP in such mixed interval-valued fuzzy network. The proposed algorithm is illustrated via two applications of SP problems in wireless sensor networks and then the results are compared with those derived from genetic and particle swarm optimization (PSO) algorithms, based on three indexes convergence iteration, convergence time and run time. The obtained results confirm that the MABC algorithm has less convergence iteration, convergence time and implementation time compared to GA and PSO algorithm.

关键词： Shortest path problem Mixed interval-valued fuzzy numbers artificial bee colony algorithm Wireless sensor network

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Sizing and layout optimization of truss structures with artificial bee colony algorithm

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STRUCTURES 2021年 30卷 546-559页

作者： Jawad, Farqad K. J. Ozturk, Celal Wang, Dansheng Mahmood, Mohammed Al-Azzawi, Osama Al-Jemely, Anas Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Hubei Peoples R China Erciyes Univ Dept Comp Engn Kayseri Turkey Univ Diyala Dept Civil Engn Diyala Iraq Huazhong Univ Sci & Technol Sch Mech Engn Wuhan Hubei Peoples R China

In this paper, a swarm intelligence-based optimization technique called artificial bee colony algorithm (ABC) is employed in combined optimization of truss structures. The objective is to optimize the layout and members size of truss structures with displacement, stress and buckling constraints. The ABC is based on simulating the intelligent foraging behavior of honey bees. The nodal coordinates of the joints and the cross-sectional areas of the members for the truss structure system are the design variables of shape and size optimization, respectively. Allowable stress, Euler buckling stress, and displacement are considered as the problem constraints. The efficiency of the ABC is tested in four benchmark structural optimization problems. The results clearly reveal the superiority of ABC over other algorithms in terms of optimized weight, standard deviation and number of structural analyses. The ABC demonstrates a robust performance with a 100% success rate.

关键词： artificial bee colony algorithm Structural design Truss optimization Sizing optimization Shape optimization

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