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Hybrid discrete artificial bee colony algorithm with threshold acceptance criterion for traveling salesman problem

INFORMATION SCIENCES 2017年 421卷 70-84页

作者： Zhong, Yiwen Lin, Juan Wang, Lijin Zhang, Hui Fujian Agr & Forestry Univ Coll Comp & Informat Sci Fuzhou 350002 Fujian Peoples R China Univ Louisville Dept Comp Engn & Comp Sci Louisville KY 40217 USA

artificial bee colony (ABC) algorithm, which has explicit strategies to balance intensification and diversification, is a smart swarm intelligence algorithm and was first proposed for continuous optimization problems. In this paper, a hybrid discrete ABC algorithm, which uses acceptance criterion of threshold accepting method, is proposed for Traveling Salesman Problem (TSP). A new solution updating equation, which can learn both from other bees and from features of problem at hand, is designed for the TSP. Aiming to enhance its ability to escape from premature convergence, employed bees and onlooker bees use threshold acceptance criterion to decide whether or not to accept newly produced solutions. Systematic experiments were performed to show the advantage of the new solution updating equation, to verify the necessity of using non-greedy acceptance strategy for keeping sufficient diversity, to compare different selection schemes for onlooker bees, and to analyze the contribution of scout bee. Comparison experiments performed on a wide range of benchmark TSP instances have shown that the proposed algorithm is better than other ABC-based algorithms and is better than or competitive with many other state-of-the-art algorithms. (C) 2017 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Traveling salesman problem Threshold acceptance criterion Collective behavior Selection schemes

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DE-caABC: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第1-4期90卷 1085-1103页

作者： Zhou, Jiajun Yao, Xifan South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Guangdong Peoples R China

Cloud manufacturing (CMfg) is a new type of service-oriented manufacturing paradigm, in which all kinds of manufacturing resources are encapsulated as manufacturing services (MSs) and can be invoked by customers on demand. Manufacturing service composition (MSC) is a key technology in CMfg for creating value-added services to complete complicated manufacturing tasks by aggregating qualified MSs together. However, current MSC approaches have some drawbacks and there still exist some issues remained to be solved: (1) large quantities of candidate services increase the complexity of service dynamic composition, which poses scalability concerns and on-demand efficient solutions;(2) the service domain features (e.g., service prior, correlation, and similarity) that have a strong influence on the efficiency of service composition are not considered adequately, which causes undesirable efficiency in practical service applications;and (3) dynamic characteristics of QoS (quality of service) values in an open network environment are not considered adequately. To effectively address such problems, this paper first proposes a context-aware artificial bee colony (caABC) algorithm based on the principle of ABC and service features in the cloud environment. Then the differential evolution-enhanced caABC, i.e., the so-called DE-caABC, is designed to increase the searching performance of ABC further. Additionally, dynamics of trust QoS is investigated with the introduction of time decay function. Finally, the feasibility and effectiveness of DE-caABC are validated through the experiments.

关键词： Cloud manufacturing Manufacturing service composition Service domain feature Quality of service Differential evolution Context awareness artificial bee colony algorithm

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Estimating transportation energy demand in Turkey using the artificial bee colony algorithm

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ENERGY 2017年 122卷 301-310页

作者： Sonmez, Mustafa Akgungor, Ali Payidar Bektas, Salih Aksaray Univ Fac Engn Dept Civil Engn TR-68100 Aksaray Turkey Kirikkale Univ Fac Engn Dept Civil Engn TR-71451 Kirikkale Turkey

In this study, three different mathematical models were proposed to estimate transportation energy demand of Turkey using the artificial bee colony algorithm. In the development of the models, gross domestic product, population and total annual vehicle-km were taken as parameters. For transportation energy demand estimations, linear, exponential and quadratic forms of mathematical expressions were used. A 44-year-old historical data from 1970 to 2013 were utilized for the training and testing stages of the models. The performances of the models were then evaluated by six different global error measurement approaches. The models that were developed were used in two possible scenarios to forecast transportation energy demand of Turkey for a 21-year period from 2014 to 2034. artificial bee colony algorithm revealed the suitability of the optimization method for transportation energy planning and policy developments in Turkey. Furthermore, the results obtained from scenarios indicated that the energy demand of Turkey will be double that of 2013 by 2034. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Transportation energy demand artificial bee colony algorithm Transportation energy demand modeling Turkey

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A grey artificial bee colony algorithm

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APPLIED SOFT COMPUTING 2017年 60卷 1-17页

作者： Xiang, Wan-li Li, Yin-zhen Meng, Xue-lei Zhang, Chun-min An, Mei-qing Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Gansu Peoples R China

artificial bee colony (ABC) algorithm is a very popular population-based algorithm. Unfortunately, there exists a shortcoming of slow convergence rate, which partly results from random choices of neighbor individuals regarding its solution search equation. A novel scheme for the choice of neighbors is introduced based on grey relational degrees between a current individual and its neighbors to overcome the insufficiency. Then, the chosen neighbor is used to guide the search process. Additionally, inspired by differential evolution, a solution search equation called ABC/rand/2 is employed to balance the previous exploitation and a new perturbation scheme is also employed. What is more, solution search equations using information of the best individual, an opposition-based learning method and a chaotic initialization technique are also integrated into the proposed algorithm called grey artificial bee colony algorithm (GABC for short). Subsequently, the effectiveness and efficiency of GABC are validated on a test suite composed of fifty-seven benchmark functions. Furthermore, it is also compared with a few state-of-the-artalgorithms. The related experimental results show the effectiveness and superiority of GABC. (C) 2017 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Grey relational analysis Opposition based learning Chaotic initialization Numerical optimization

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An artificial bee colony algorithm with Random Location Updating

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SCIENTIFIC PROGRAMMING 2018年第Pt.2期2018卷 1-9页

作者： Sun, Lijun Chen, Tianfei Zhang, Qiuwen Henan Univ Technol Sch Elect Engn Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450002 Henan Peoples R China

As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage of simple structure, less control parameters, and excellent performance characteristics and can be applied to neural network, parameter optimization, and so on. In order to further improve the exploration ability of ABC, an artificial bee colony algorithm with random location updating (RABC) is proposed in this paper, and the modified search equation takes a random location in swarm as a search center, which can expand the search range of new solution. In addition, the chaos is used to initialize the swarm population, and diversity of initial population is improved. Then, the tournament selection strategy is adopted to maintain the population diversity in the evolutionary process. Through the simulation experiment on a suite of unconstrained benchmark functions, the results show that the proposed algorithm not only has stronger exploration ability but also has better effect on convergence speed and optimization precision, and it can keep good robustness and validity with the increase of dimension.

关键词： performance characteristics Range searching Particle swarm optimization artificial bee colony algorithm Random activity based costing simulation test honey collection dike swarms information exchange

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Training ANFIS by using the artificial bee colony algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第3期25卷 1669-1679页

作者： Karaboga, Dervis Kaya, Ebubekir Erciyes Univ Dept Comp Engn Fac Engn Kayseri Turkey Nevsehir Haci Bektas Veli Univ Nevsehir Vocat Coll Dept Comp Technol Nevsehir Turkey

In this study, a new adaptive network-based fuzzy inference system (ANFIS) training algorithm, the artificial bee colony (ABC) algorithm, is presented. Antecedent and conclusion parameters existing in the structure of ANFIS are optimized with the ABC algorithm and ANFIS training is realized. Identification of a set of nonlinear dynamic systems is performed in order to analyze the suggested training algorithm. The ABC algorithm is operated 30 times for each identification case and the average root mean square error (RMSE) value is obtained. Training RMSE values calculated for the four examples considered are 0.0325, 0.0215, 0.0174, and 0.0294, respectively. In addition, test error values for the same cases are respectively computed as 0.0270, 0.0186, 0.0167, and 0.0435. The results obtained are compared with those of known neuro-fuzzy-based methods frequently used in the literature in identification studies of nonlinear systems. It is shown that ANFIS can be trained successfully by using the ABC algorithm for the identification of nonlinear systems.

关键词： ANFIS swarm intelligence artificial bee colony algorithm nonlinear system identification

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A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization

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INFORMATION SCIENCES 2017年 414卷 53-67页

作者： Cui, Laizhong Li, Genghui Zhu, Zexuan Lin, Qiuzhen Wen, Zhenkun Lu, Nan Wong, Ka-Chun Chen, Jianyong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

The artificial bee colony (ABC) algorithm is a new branch of evolutionary algorithms (EAs) that is inspired by the collective foraging behavior of real honey bee colonies. Due to its foraging model and its solution search equation, ABC generally performs well in exploration but badly in exploitation. To address this concerning issue and obtain a good balance between exploration and exploitation, in this paper, we mainly introduce into the ABC an adaptive method for the population size (AMPS). AMPS is inspired by the natural principle that the size of a population is affected by the availability of food resources. When food resources are abundant, a population tends to expand;otherwise, a decrease in the availability of food resources leads to a shrinkage in the population size. Specifically, when the algorithm performs well in exploration, AMPS will shrink the population to enhance exploitation by periodically removing some inferior solutions that have low success rates. In contrast, AMPS will enlarge the population to improve exploration by introducing some reserved solutions. Furthermore, to make AMPS perform better, we design a new solution search equation for employed bees and onlooker bees. Moreover, we also improve the probability model of the onlooker bees. By embedding our three proposed algorithmic components into ABC, we propose a novel ABC variant, called APABC. To demonstrate the performance of APABC, we compare APABC with some state-of-the-art ABC variants and some other non-ABC methods on 22 scalable benchmark functions and 30 CEC2014 test functions. The simulation results show that APABC is better than or at least competitive with the competitors in terms of its solution quality, robustness and convergence speed. (C) 2017 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Exploration and exploitation Adaptive method for the population size Solution search equation Probability model

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Identification-based simplified model of large container ships using support vector machines and artificial bee colony algorithm

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APPLIED OCEAN RESEARCH 2017年 68卷 249-261页

作者： Zhu, Man Hahn, Axel Wen, Yuan-Qiao Bolles, Andre Carl von Ossietzky Univ Oldenburg Comp Sci D-26121 Oldenburg Germany Wuhan Univ Technol Sch Nav Wuhan 430063 Hubei Peoples R China Hubei Key Lab Inland Shipping Technol Wuhan 430063 Hubei Peoples R China Inst Informat Technol D-26121 Oldenburg Germany

The 6 degrees of freedom (DOE) model with a high degree of complexity for capturing ship dynamics is generally able to track the nonlinear and coupling dynamics of ships. However, the 6 DOF model makes challenges in estimating model coefficients and designing the model-based control. Therefore, simplified ship dynamic models within allowed accuracy are essential. This paper simplified the 6 DOF nonlinear dynamic model of ships into two decoupled models including the speed model and the steering model through reasonable assumptions. Those models were tested through maneuvering simulations of a container ship with a 4 DOF dynamic model. Support vector machines (SVM) optimized by the artificial bee colony algorithm (ABC) was used to identify parameters of speed and steering models by analyzing the rudder angle, propeller shaft speed, surge and sway velocities, and yaw rate from simulated data extracted from a series of maneuvers made by the container ship. Comparisons with the first order linear and nonlinear Nomoto models show that the simplified nonlinear steering model can capture more complicated dynamics and performs better. Additionally, comparisons among three different parameter identification methods demonstrate similar identification results but the different performance involving the applicability and effectiveness. SVM optimized by ABC is relatively convenient and effective for parameter identification of ship simplified dynamic models. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Nonlinear ship dynamics Simplified ship models Large ships Support vector machines artificial bee colony algorithm Parameter identification

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An enhanced artificial bee colony algorithm with adaptive differential operators

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APPLIED SOFT COMPUTING 2017年 58卷 480-494页

作者： Liang, Zhengping Hu, Kaifeng Zhu, Quanxiang Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

artificial bee colony algorithm (ABC) has been shown to be very effective to solve global optimization problems (GOPs). However, ABC performs well in exploration but relatively poorly in exploitation resulting in a slow convergence when it is used to handle complex GOPs. Differential evolution (DE) benefits from its differential operators, namely mutation operator and crossover operator, which could perturb multiple variables simultaneously and has shown a fast convergence speed. In order to improve ABC's exploitation ability and accelerate its convergence, in this paper, we propose an enhanced ABC algorithm named ABCADE, which remedy the limitation of ABC by exploiting the advantage of differential operators. Particularly, in ABCADE, the employed bees employ differential operators to produce candidate solutions with an increasing probability, and the two important parameters (scale factor F and crossover rate CR) of differential operators are adaptively adjusted through Gaussian distribution. Moreover, to significantly differentiate the good solutions and bad solutions in a population, and put more effort in the exploitation around the good solutions, we design a new selection probability method for onlooker bees. To verify the performance of ABCADE, we compare ABCADE with other representative state-of-the-art ABC and DE algorithms, the comparison results on a set of 22 benchmark functions with various dimension sizes demonstrate that ABCADE obtains superior or comparable performance to other algorithms. (C) 2017 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Differential operators Selection probability Global optimization

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Optimal parameters and performance of artificial bee colony algorithm for minimum cost design of reinforced concrete frames

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ENGINEERING STRUCTURES 2017年 151卷 802-820页

作者： Tapao, Assanai Cheerarot, Raungrut Mahasarakham Univ Concrete & Comp Res Unit Civil Engn Fac Engn Kantarawichai 44150 Mahasarakham Thailand

This study investigates the effect of controlling parameters on the performance of an artificial bee colony (ABC) algorithm in the optimum design of reinforced concrete frames under combination loads according to the ACI318-08 building code requirements for structural concrete. The objective function is the total cost of reinforced concrete frames, which consists of concrete cost, reinforcing bars cost, and formwork cost. The cross section of structure, diameter, and number of reinforcing bars are considered as the design variables. The effect of the number of bees, food source quantities, trial limit, and stopping condition on the cost design are studied and presented with statistical results. Three design examples are collected from related literature to evaluating the performance of ABC algorithm. The results demonstrate that the number of trial limits is critical to the quality of food source, while the numbers of bees, food sources, and trial limits impact the obtained optimum solution and Usage time. The statistical results reveal that when the food source quantities are lower than the number of bees, ABC algorithm provides high performance for all the design examples. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Reinforced concrete frames Cost optimization Constraints optimization artificial bee colony algorithm

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