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An artificial bee colony algorithm for learning Bayesian networks

SOFT COMPUTING 2013年第6期17卷 983-994页

作者： Ji, Junzhong Wei, Hongkai Liu, Chunnian Beijing Univ Technol Coll Comp Sci & Technol Beijing Municipal Key Lab Multimedia & Intelligen Beijing Peoples R China

One basic approach to learn Bayesian networks (BNs) from data is to apply a search procedure to explore the set of candidate networks for the database in light of a scoring metric, where the most popular stochastic methods are based on some meta-heuristic mechanisms, such as Genetic algorithm, Evolutionary Programming and Ant colony Optimization. In this paper, we have developed a new algorithm for learning BNs which employs a recently introduced meta-heuristic: artificial bee colony (ABC). All the phases necessary to tackle our learning problem using this meta-heuristic are described, and some experimental results to compare the performance of our ABC-based algorithm with other algorithms are given in the paper.

关键词： Bayesian networks Structure learning Stochastic search artificial bee colony algorithm

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A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion

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APPLIED MATHEMATICAL MODELLING 2013年第10-11期37卷 6758-6779页

作者： Tasgetiren, M. Fatih Pan, Quan-Ke Suganthan, P. N. Oner, Adalet Yasar Univ Dept Ind Engn Izmir Turkey Liaocheng Univ Coll Comp Sci Liaocheng Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this paper, we present a discrete artificial bee colony algorithm to solve the no-idle permutation flowshop scheduling problem with the total tardiness criterion. The no-idle permutation flowshop problem is a variant of the well-known permutation flowshop scheduling problem where idle time is not allowed on machines. In other words, the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions: First of all, a discrete artificial bee colony algorithm is presented to solve the problem on hand first time in the literature. Secondly, some novel methods of calculating the total tardiness from make-span are introduced for the no-idle permutation flowshop scheduling problem. Finally, the main contribution of the paper is due to the fact that a novel speed-up method for the insertion neighborhood is developed for the total tardiness criterion. The performance of the discrete artificial bee colony algorithm is evaluated against a traditional genetic algorithm. The computational results show its highly competitive performance when compared to the genetic algorithm. Ultimately, we provide the best known solutions for the total tardiness criterion with different due date tightness levels for the first time in the literature for the Taillard's benchmark suit. (C) 2013 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm No-idle permutation flowshop scheduling problem Metaheuristics Evolutionary algorithms Genetic algorithm

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Identification of structural models using a modified artificial bee colony algorithm

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COMPUTERS & STRUCTURES 2013年第Jan.期116卷 59-74页

作者： Sun, Hao Lus, Hilmi Betti, Raimondo Columbia Univ Dept Civil Engn & Engn Mech New York NY 10027 USA Bogazici Univ Dept Civil Engn TR-34342 Istanbul Turkey

A modified version of the artificial bee colony (ABC) algorithm is presented to identify structural systems. ABC is a heuristic algorithm with simple structure, ease of implementation and robustness. A nonlinear factor for convergence control is introduced in the algorithm to enhance the balance of global and local searches. To investigate the applicability of this proposed technique to system identification, three examples are studied under different conditions regarding data availability, noise pollution level, priori knowledge of parameters, etc. Simulation results show the proposed technique produces excellent parameter estimation, even with few measurements and high noise corruptions. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Parameter estimation Structural system identification artificial bee colony algorithm Optimization

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An approximation algorithm for fuzzy polynomial interpolation with artificial bee colony algorithm

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APPLIED SOFT COMPUTING 2013年第4期13卷 1997-2002页

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

In this paper, a novel approximation algorithm for fuzzy polynomial interpolation using artificial bee colony algorithm to interpolate fuzzy data is discussed. However, we use our modified ABC (MABC;Mansouri et al. [13]) to perform the required task. Some examples (including the benchmark functions Griewank and Rastrigin) illustrate the rationality of the method and the validity of the solution. We compare our results with other methods including Genetic algorithm (GA), Particle Swarm algorithm (PSO). The results show that proposed method outperforms the other algorithms. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Fuzzy polynomial interpolation artificial bee colony algorithm

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Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization

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APPLIED SOFT COMPUTING 2013年第12期13卷 4676-4694页

作者： Das, Swagatam Biswas, Subhodip Kundu, Souvik Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India

Evolutionary computation (EC) paradigm has undergone extensions in the recent years diverging from the natural process of genetic evolution to the simulation of natural life processes exhibited by the living organisms. bee colonies exemplify a high level of intrinsic interdependence and co-ordination among its members, and algorithms inspired from the bee colonies have gained recent prominence in the field of swarm based metaheuristics. The artificial bee colony (ABC) algorithm was recently developed, by simulating the minimalistic foraging model of honeybees in search of food sources, for solving real-parameter, non-convex, and non-smooth optimization problems. The single parameter perturbation in classical ABC resulted in fairly commendable performance for simple problems without epistasis of variables ( separable). However, it suffered from narrow search zone and slow convergence which eventually led to poor exploitation tendency. Even with the increase in dimensionality, a significant deterioration was observed in the ability of ABC to locate the optimum in a huge search volume. Some of the probable shortcomings in the basic ABC approach, as observed, are the single parameter perturbation instead of a multiple one, ignoring the fitness to reward ratio while selecting food sites, and most importantly the absence of environmental factors in the algorithm design. Research has shown that spatial environmental factors play a crucial role in insect locomotion and foragers seem to learn the direction to be undertaken based on the relative analysis of its proximal surroundings. Most importantly, the mapping of the forager locomotion from three dimensional search spaces to a multidimensional solution space calls forth the implementation of multiple modification schemes. Based on the fundamental observation pertaining to the dynamics of ABC, this article proposes an improved variant of ABC aimed at improving the optimizing ability of the algorithm over an extended set of

关键词： Swarm intelligence artificial bee colony algorithm Biologically-inspired optimization Exploration Exploitation Fitness learning

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System identification and control of heat integrated distillation column using artificial bee colony based support vector regression

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CHEMICAL ENGINEERING COMMUNICATIONS 2022年第10期209卷 1377-1396页

作者： Jaleel, E. Abdul Anzar, S. M. beegum, T. Rehannara Shahid, P. A. Mohamed Citrus Informat Ernakulam Kochi 682021 Kerala India TKM Coll Engn Dept Elect & Commun Engn Kollam Kerala India TKM Coll Engn Dept Comp Sci & Engn Kollam Kerala India TKM Coll Engn Dept Mech Engn Kollam Kerala India

Distillation is a high-energy process widely employed in separating fluid mixtures in the oil and gas industries. Heat integration is one of the practical approaches for energy saving in the distillation columns. Proper identification or modeling of heat-integrated distillation column (HIDC) is employed to predict the composition of fluid mixtures. The nonlinear modeling of HIDC is highly challenging, and methods based on the first principles are not effective in coping with the nonlinearities. Hence, a novel, non-parametric support vector regression (SVR) approach is proposed for system identification and control of HIDC in this work. SVR parameters were optimized using artificial bee colony (ABC) algorithm, which resulted in better performance over other meta-heuristic algorithms. Moreover, the SVR model demonstrated better performance than the artificial neural network models in root mean square error (RMSE) and regression coefficient (R). RMSE and R values for ABC-SVR were found to be 0.0010 and 0.99992, respectively, with the validation dataset. The performance of the SVR and PID controllers are also compared. Integral square error (ISE), integral average error (IAE), integral time square error (ITSE), and integral time average error (ITAE) are the comparison metrics employed, which yielded minimal values of 5.26x10(-5), 2.98x10(-2), 5.15x10(-4), and 4.61x10(-1), respectively, for the SVR controller. The proposed model outperforms all other related methods, and it can be used to predict the mole fraction of Benzene in Benzene-Toluene HIDC accurately.

关键词： artificial bee colony algorithm control fluid separation heat integrated distillation column support vector regression system identification

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A modified artificial bee colony algorithm for order acceptance in two-machine flow shops

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2013年第1期141卷 14-23页

作者： Wang, Xiuli Xie, Xingzi Cheng, T. C. E. Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Jiangsu Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Kowloon Hong Kong Peoples R China

We consider a two-stage make-to-order production system characterized by limited production capacity and tight order due dates. We want to make joint decisions on order acceptance and scheduling to maximize the total net revenue. The problem is computationally intractable. In view of the fact that artificial bee colony algorithm has been shown to be an effective evolutionary algorithm to handle combinatorial optimization problems, we first conduct a pilot study of applying the basic artificial bee colony algorithm to treat our problem. Based on the results of the pilot study and the problem characteristics, we develop a modified artificial bee colony algorithm. The experimental results show that the modified artificial bee colony algorithm is able to generate good solutions for large-scale problem instances. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Scheduling Order acceptance artificial bee colony algorithm Revenue

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Two modified versions of artificial bee colony algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2013年 225卷 601-609页

作者： Alizadegan, Amir Asady, Babak Ahmadpour, Mohammad Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran Islamic Azad Univ Arak Branch Dept Comp Engn Arak Iran Islamic Azad Univ Maku Branch Dept Math Maku Iran

artificial bee colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms used in optimization problems. ABC simulates the intelligent foraging behavior of a honeybee swarm. In this paper, two aspects of ABC algorithm are modified and new configurations are used. The modified versions are tested on some well-known benchmark functions. Results show that the new changes have positive effects on the performance of ABC algorithm. (C) 2013 Published by Elsevier Inc.

关键词： Swarm intelligence Optimization algorithms artificial bee colony algorithm Numerical function optimization Unconstrained optimization

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Application of the artificial bee colony algorithm-based projection pursuit method in statistical rock mass stability estimation

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ENVIRONMENTAL EARTH SCIENCES 2013年第8期68卷 2337-2345页

作者： Li, Haojin Li, Junjie Kang, Fei Dalian Univ Technol Dept Hydraul Engn Fac Infrastruct Engn Dalian City Liaoning Provin Peoples R China

Because of the complexity of factors that affect rock mass stability, the design and decision-making in related engineering cannot rely solely on theoretical analysis and numerical calculation, but depend on the comprehensive judgment of experts. In pursuit of a statistical approach that may improve this disparity, an artificial bee colony algorithm-based projection pursuit (ABC-PP) method is presented for rock mass stability determination. The ABC-PP method is a powerful tool to deal with high-dimension problems, which characterize rock mass stability assessment practice. Two experiments are employed to demonstrate the efficiency of the ABC-PP method. In the first case, the state of stability is classified at two levels: stable and failed, whereas in the second case stability is classified at five levels (1-5) to test the capability of multi-level prediction of the ABC-PP method. Results show that the ABC-PP method could predict the rock mass stability accurately and may also provide the relative importance of specific controls on stability.

关键词： Rock mass stability estimation Projection pursuit method artificial bee colony algorithm Multi-factor comprehensive evaluation

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An efficient and robust artificial bee colony algorithm for numerical optimization

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COMPUTERS & OPERATIONS RESEARCH 2013年第5期40卷 1256-1265页

作者： Xiang, Wan-li An, Mei-qing Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China Tianjin Univ Inst Syst Engn Tianjin 300072 Peoples R China

artificial bee colony (ABC) algorithm has already shown more effective than other population-based algorithms. However, ABC is good at exploration but poor at exploitation, which results in an issue on convergence performance in some cases. To improve the convergence performance of ABC, an efficient and robust artificial bee colony (ERABC) algorithm is proposed. In ERABC, a combinatorial solution search equation is introduced to accelerate the search process. And in order to avoid being trapped in local minima, chaotic search technique is employed on scout bee phase. Meanwhile, to reach a kind of sustainable evolutionary ability, reverse selection based on roulette wheel is applied to keep the population diversity. In addition, to enhance the global convergence, chaotic initialization is used to produce initial population. Finally, experimental results tested on 23 benchmark functions show that ERABC has a very good performance when compared with two ABC-based algorithms. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Initialization based on chaos Reverse selection based on roulette wheel Solution search equation Chaotic search

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