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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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An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第9-12期60卷 1111-1123页

作者： Wang, Ling Zhou, Gang Xu, Ye Liu, Min Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China

In this paper, an enhanced Pareto-based artificial bee colony (EPABC) algorithm is proposed to solve the multi-objective flexible job-shop scheduling problem with the criteria to minimize the maximum completion time, the total workload of machines, and the workload of the critical machine simultaneously. First, it uses multiple strategies in a combination way to generate the initial solutions as the food sources with certain quality and diversity. Second, exploitation search procedures for both the employed bees and the onlooker bees are designed to generate the new neighbor food sources. Third, crossover operators are designed for the onlooker bee to exchange useful information. Meanwhile, it uses a Pareto archive set to record the nondominated solutions that participate in crossover with a certain probability. To enhance the local intensification, a local search based on critical path is embedded in the onlooker bee phase, and a recombination and select strategy is employed to determine the survival of the individuals. In addition, population is suitably adjusted to maintain diversity in scout bee phase. By using Taguchi method of design of experiment, the influence of several key parameters is investigated. Simulation results based on the benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed EPABC algorithm.

关键词： Flexible job-shop scheduling problem Multi-objective optimization artificial bee colony algorithm Machine assignment Operation sequence Critical path

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Dual-Population artificial bee colony algorithm for Joint Observation Satellite Mission Planning Problem

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IEEE ACCESS 2022年 10卷 28911-28921页

作者： Jiang, Xuemei Song, Yanjie Xing, Lining Xidian Univ Sch Elect Engn Xian 710126 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China

Earth observation satellites provide services for users by taking images. The rapid increase in the number of satellites and missions makes mission planning more difficult. In order to solve this problem, this paper adopts a multi-population evolutionary algorithm. First, a mixed-integer programming model of the joint observation satellites mission planning problem (JOSMPP) is constructed. After that, a dual-population artificial bee colony algorithm (DPABC) and a heuristic task scheduling algorithm are proposed. Two learning-based nectar generation methods are used to guide the direction of population optimization. The search population is used to lead the nectar search at the stage of employed bees and supplement population is used to improve search performance. According to the performance of these two populations, candidate populations are generated to realize the search of onlooker bees. After each generation of population search, the composition of the two populations is adjusted according to the performance. Finally, the experimental results show that the DPABC algorithm has more advantages than multiple state-of-the-art algorithms to solve the joint mission planning problem of earth observation satellites.

关键词： Satellites Task analysis Planning Statistics Sociology Earth artificial bee colony algorithm Observation satellite dual population artificial bee colony algorithm planning heuristic

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Cluster based wireless sensor network routing using artificial bee colony algorithm

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WIRELESS NETWORKS 2012年第7期18卷 847-860页

作者： Karaboga, Dervis Okdem, Selcuk Ozturk, Celal Erciyes Univ Fac Engn Dept Comp Engn TR-38039 Kayseri Turkey

Due to recent advances in wireless communication technologies, there has been a rapid growth in wireless sensor networks research during the past few decades. Many novel architectures, protocols, algorithms, and applications have been proposed and implemented. The efficiency of these networks is highly dependent on routing protocols directly affecting the network life-time. Clustering is one of the most popular techniques preferred in routing operations. In this paper, a novel energy efficient clustering mechanism, based on artificial bee colony algorithm, is presented to prolong the network life-time. artificial bee colony algorithm, simulating the intelligent foraging behavior of honey bee swarms, has been successfully used in clustering techniques. The performance of the proposed approach is compared with protocols based on LEACH and particle swarm optimization, which are studied in several routing applications. The results of the experiments show that the artificial bee colony algorithm based clustering can successfully be applied to WSN routing protocols.

关键词： Wireless sensor networks Cluster based routing artificial bee colony algorithm

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Optimization of injection scheme to maximizing cumulative oil steam ratio based on improved artificial bee colony algorithm

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2019年 173卷 371-380页

作者： Ni, Hong-mei Liu, Yong-jian Fan, Ying-cai Northeast Petr Univ Sch Comp & Informat Technol Daqing 163318 Peoples R China Northeast Petr Univ MOE Key Lab Enhanced Oil Recovery Daqing 163318 Peoples R China PetroChina Liaohe Oilfield Co Panjin 124010 Liaoning Peoples R China

To address the optimization of injection scheme of steam flooding, an injection optimization method of steam flooding based on improved artificial bee colony (IABC) algorithm is proposed. Firstly, a mathematical model of steam flooding scheme optimization is established, and then the IABC algorithm is used to solve the model and optimize the injection scheme of steam flooding. In the IABC algorithm, we introduce a chaos optimization operator to generate initial solutions and apply an adaptive recombination operator to ensure the diversity of the population. In addition, we use a random perturbation operator based on the evolutionary stagnation step to improve the search ability of artificial bee colony algorithm. The experimental results show that the established model is accurate and the optimization algorithm is effective. It can be seen that the injection scheme of steam flooding optimized by the IABC algorithm can increase more cumulative oil steam ratio and cumulative oil production than that optimized by similar algorithms. The proposed method can obtain maximum recovery, and can significantly improve economic efficiency.

关键词： artificial bee colony algorithm Steam flooding Chaos optimization operator Adaptive recombination Random perturbation

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Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey

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ENERGY 2014年 69卷 638-647页

作者： Uzlu, Ergun Akpinar, Adem Ozturk, Hasan Tahsin Nacar, Sinan Kankal, Murat Karadeniz Tech Univ Fac Engn Dept Civil Engn TR-61080 Trabzon Turkey Uludag Univ Fac Engn Dept Civil Engn TR-16059 Bursa Turkey

The primary objective of this study was to apply the ANN (artificial neural network) model with the ABC (artificial bee colony) algorithm to estimate annual hydraulic energy production of Turkey. GEED (gross electricity energy demand), population, AYT (average yearly temperature), and energy consumption were selected as independent variables in the model. The first part of the study compared ANN-ABC model performance with results of classical ANN models trained with the BP (back propagation) algorithm. Mean square and relative error were applied to evaluate model accuracy. The test set errors emphasized positive differences between the ANN-ABC and classical ANN models. After determining optimal configurations, three different scenarios were developed to predict future hydropower generation values for Turkey. Results showed the ANN-ABC method predicted hydroelectric generation better than the classical ANN trained with the BP algorithm. Furthermore, results indicated future hydroelectric generation in Turkey will range from 69.1 to 76.5 TWh in 2021, and the total annual electricity demand represented by hydropower supply rates will range from 14.8% to 18.0%. However, according to Vision 2023 agenda goals, the country plans to produce 30% of its electricity demand from renewable energy sources by 2023, and use 20% less energy than in 2010. This percentage renewable energy provision cannot be accomplished unless changes in energy policy and investments are not addressed and implemented. In order to achieve this goal, the Turkish government must reconsider and raise its own investments in hydropower, wind, solar, and geothermal energy, particularly hydropower. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Hydropower generation Neural networks Turkey

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Multi-objective hybrid artificial bee colony algorithm enhanced with L,vy flight and self-adaption for cloud manufacturing service composition

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APPLIED INTELLIGENCE 2017年第3期47卷 721-742页

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

Service composition and optimal selection (SCOS) is a key problem in cloud manufacturing (CMfg). The present study proposed a multi-objective hybrid artificial bee colony (HABC) algorithm to address the SCOS problem in consideration of both quality of service (QoS) and energy consumption, to which an improved solution update equation with multiple dimensions of perturbation was adopted in the employed bee phase. Likewise, a cuckoo search-inspired L,vy flight was employed in the onlooker bee phase to overcome basic artificial bee colony (ABC) drawbacks such as poor exploitation and slow convergence. Moreover, a parameter adaptive strategy was applied to adjust the perturbation rate and step size of the L,vy flight to improve the performance of the algorithm. The proposed algorithm was first tested on 21 multi-objective benchmark problems and compared with four other state-of-the-art multi-objective evolutionary algorithms (MOEAs). The effect of the improvement strategies was then experimentally verified. Finally, the HABC was applied to solve multiscale SCOS problems using comparison experiments, which resulted in more competitive results and outperformed other MOEAs.

关键词： Cloud manufacturing Multi-objective optimization Levy flight artificial bee colony algorithm Service composition

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Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources

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ENERGY REPORTS 2021年 7卷 8912-8928页

作者： Saeed, Muhammad Hammad Fangzong, Wang Salem, Sultan Khan, Yousaf Ali Kalwar, Basheer Ahmad Fars, Ashk China Three Gorges Univ Coll Elect Engn & New Energy Res Ctr Microgrid New Energy 8 Daxue Rd Yichang 443002 Hubei Peoples R China Univ Birmingham Birmingham Business Sch BBS Dept Econ Birmingham W Midlands England Hazara Univ Mansehra Dept Math & Stat Mansehra 23010 Pakistan Sunlife Co Elect & Elect Engn Dept Baku Azerbaijan

A two-stage planning form of multi-energy supply optimization such as power, cooling, and heating is presented in this paper as a micro energy grid (MEG) To cover the effect of uncertainty in renewable energy sources (RES), the scheduling cycle is considered in this paper. Next, the results of the day-ahead prediction are considered as random variables for the upper-layer model. To realize the random variables at the lower layer, the revised model of energy storage and the demand response (DR) planning model are considered. Finally, the modified version of the artificial bee colony (ABC) algorithm is utilized to find the optimal solution. The improved ABC algorithm is a shape-memory method based on the collective intelligence and behavior of bees in a colony for finding the best nutrition source. In the improved ABC algorithm, with information exchange between the bees, based on Newton's law of universal gravitation, the full potential of this algorithm is used to find the optimal solution given the constraints applied to the system. The proposed method is applied to a real system and the results show that the two-stage optimization algorithm and the proposed intelligent algorithm obtained the simultaneous optimization of different energy forms. The obtained numerical analysis results in test cases prove the following points: (1) The optimal synergistic supply of multiple energy forms has been provided based on the two-stage optimization algorithm and solution approach. (2) The surplus energy can be converted to natural gas by the power-to-gas converter (P2G) based on power cascade conversion in a multi-directional mode. (3) To get some revenue, the MEG is flexible enough to cooperate with the upper-grade energy network. (4) The DR-based price can smooth the load shape and increase the MEG operation revenue using some supplementary features. Also, P2G can sequentially develop the flexible multidirectional energy conversion in energy - gas - energy cooling as a casca

关键词： Optimization Microgrid planning Renewable resources artificial bee colony algorithm

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Kurtosis forecasting of bearing vibration signal based on the hybrid model of empirical mode decomposition and RVM with artificial bee colony algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第11期42卷 5011-5018页

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

Accurate prediction for kurtosis of bearing vibration signal is helpful to find out the fault of bearing as soon as possible. As it is difficult to obtain an appropriate embedding dimension in creating directly the prediction model of kurtosis of bearing vibration signal by relevance vector machine (RVM), the hybrid model of empirical mode decomposition and RVM with artificial bee colony algorithm (EMD-ABCRVM) is proposed for kurtosis forecasting of bearing vibration signal. The seven decomposed signals with different frequency range can be obtained by empirical mode decomposition for kurtosis of bearing vibration signal. The prediction models of the seven decomposed signals can be established by RVNI with their each appropriate embedding dimension, and artificial bee colony algorithm (ABC) is used to select the appropriate kernel parameters of their RVM models. Thus, each RVM model of the seven decomposed signals has appropriate embedding dimension and kernel parameter. In order to show the superiority of the proposed EMD-ABCRVM method, the RVM models with several different embedding dimensions and Gaussian RBF kernel parameters are used to compare with the proposed EMD-ABCRVM method. The experimental results show that it is feasible for the proposed combination scheme to improve the prediction accuracy of RVM for kurtosis of bearing vibration signal. (C) 2015 Published by Elsevier Ltd.

关键词： Kurtosis prediction of bearing vibration signal Empirical mode decomposition Embedding dimension artificial bee colony algorithm

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Sensor network sensing coverage optimization with improved artificial bee colony algorithm using teaching strategy

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COMPUTING 2021年第7期103卷 1439-1460页

作者： Lu, Chao Li, Xunbo Yu, Wenjie Zeng, Zhi Yan, Mingming Li, Xiang Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu Peoples R China Chengdu Univ Informat Technol Sch Control Engn Chengdu Peoples R China

Considering the complexity of wireless sensor network (WSN) coverage problems, which include many variables and a large continuous search space, a WSN coverage optimization method based on an improved artificial bee colony (ABC) algorithm with teaching strategy is proposed in this paper. ABC, which is good at exploration but poor at exploitation, is improved by introducing a teaching strategy in teaching-learning-based optimization (TLBO) that has a rapid convergence but is easily trapped in a local optima. Thus, the proposed algorithm combines the advantages of ABC strong global search ability and TLBO rapid convergence. In addition, to retain the diversity and eliminate the parameter limit in ABC, a dynamic search update strategy is introduced instead of the scout bee phase of ABC. In addition to preliminary examinations with a number of benchmark functions, the performance of the algorithm is verified by solving a complicated wireless sensor network coverage problem. The simulation results verify that the proposed algorithm achieves better balance between global and local search compared with other state-of-the-art algorithms.

关键词： artificial bee colony algorithm Teaching strategy Wireless sensor network Coverage optimization

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