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A high-efficiency adaptive artificial bee colony algorithm using two strategies for continuous optimization

SWARM AND EVOLUTIONARY COMPUTATION 2019年 50卷 100549-000页

作者： Song, Xiaoyu Zhao, Ming Yan, Qifeng Xing, Shuangyun Shenyang Jianzhu Univ Informat & Control Engn Sch Shenyang 110168 Liaoning Peoples R China Chinese Acad Sci Cixi Inst Biomed Engn Ningbo Inst Ind Technol Ningbo 322100 Zhejiang Peoples R China Shenyang Jianzhu Univ Coll Sci Shenyang 110168 Liaoning Peoples R China

It has always been a problem faced by artificial bee colony (ABC) algorithm that how to adjust exploration and exploitation dynamically in the evolution process. In order to overcome this problem, this paper presents a highly efficient variant of ABC algorithm which is two-strategy adaptive. Among the two proposed search strategies, one has strong exploration ability and the other has strong exploitation ability;Based on the adaptability of the two search strategies to the problem solving and the search process, the selection probability of each search strategy is dynamically adjusted according to success rate, and then the cooperative optimization of the two search strategies is realized to improve the performance of the algorithm. It can be seen that the improved algorithm is enhanced significantly on accuracy of solution and success rate from comparing experiment results with the other state-of-the-art ABC algorithms.

关键词： artificial bee colony algorithm Search strategy Strategy adaptation Select probability Success rate

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artificial bee colony algorithm Based on Adaptive Search Equation and Extended Memory

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CYBERNETICS AND SYSTEMS 2017年第5期48卷 459-482页

作者： Mao, Mingxuan Duan, Qichang Zhang, Li Chongqing Univ Automat Coll Chongqing 400044 Peoples R China Univ Leeds Sch Elect & Elect Engn Leeds W Yorkshire England

From the perspective of psychology, a modified artificial bee colony algorithm (ABC, for short) based on adaptive search equation and extended memory (ABCEM, for short) for global optimization is proposed in this paper. In the proposed ABCEM algorithm, an extended memory factor is introduced into store employed bees' and onlooker bees' historical information comprising recent food sources, personal best food sources, and global best food sources, and the solution search equation for the employed bees is equipped with adaptive ability. Moreover, a parameter is employed to describe the importance of the extended memory. Furthermore, the extended memory is added to two solution search equations for the employed bees and the onlookers to improve the quality of food source. To evaluate the proposed algorithm, experiments are conducted on a set of numerical benchmark functions. The results show that the proposed algorithm can balance the exploration and exploitation, and can improve the accuracy of optima solutions and convergence speed compared with other current improved ABCs for global optimization in most of the tested functions.

关键词： Adaptive solution search equation artificial bee colony algorithm extended memory global optimization

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Improved artificial bee colony algorithm and its Application in Classification

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JOURNAL OF ROBOTICS AND MECHATRONICS 2018年第6期30卷 921-926页

作者： Wang, Haiquan Wei, Jianhua Wen, Shengjun Yu, Hongnian Zhang, Xiguang Zhongyuan Univ Technol Zhongyuan Petersburg Aviat Coll 41 Zhongyuan Rd Zhengzhou 450007 Henan Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn 41 Zhongyuan Rd Zhengzhou 450007 Henan Peoples R China Bournemouth Univ Fac Sci & Technol Poole BH12 5BB Dorset England

For improving the classification accuracy of the classifier, a novel classification methodology based on artificial bee colony algorithm is proposed for optimal feature and SVM parameters selection. In order to balance the ability of exploration and exploitation of traditional ABC algorithm, improvements are introduced for the generation of initial solution set and onlooker bee stage. The proposed algorithm is applied to four datasets with different attribute characteristics from UCI and efficiency of the algorithm is proved from the results.

关键词： artificial bee colony algorithm classifier SVM UCI database wrapper method

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An artificial bee colony algorithm with variable neighborhood search and tabu list for long-term carpooling problem with time window

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APPLIED SOFT COMPUTING 2019年 85卷 105814-000页

作者： Su, Sheng Zhou, Fangzheng Yu, Haijie Univ Elect Sci & Technol China Sch Software Engn Chengdu Sichuan Peoples R China Chongqing Univ Art & Sci Sch Econ & Management Chongqing Peoples R China

Carpooling is an effective sharing economy mode that can increase utilization ratio of vehicle and relieve traffic pressure in cities. A carpooling system needs to provide feasible car-sharing solution under the constraint on time window of each passenger. Moreover, fairness is important to a long-term car sharing problem. A person will not willing to share his/her car if he/she has more contribution than others. In this paper, a long-term carpooling problem with time window is investigated for a group of people with a common destination. In order to guarantee fairness, a person can be chosen as a driver only for a limited number of consecutive days. An artificial bee colony algorithm combining variable neighbor search and tabu list (ABC-VNSTL) are proposed. We designed five neighbor search strategies: one-swapping strategy, all-swapping strategy, moving strategy, passenger to driver strategy, and solution exchange strategy. The experiments results demonstrate that ABC-VNSTL can obtain better solution quality than other six algorithms in the literature. As the number of participants increases, the advantages of ABC-VNSTL increase. In addition, ABC-VNSTL algorithm is more efficient than the compared algorithms under the condition of achieving same solution quality. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Carpooling artificial bee colony algorithm Variable neighborhood search Time window

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An Elite Group Guided artificial bee colony algorithm with a Modified Neighborhood Search 15th

An Elite Group Guided Artificial Bee Colony Algorithm with a...

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15th Pacific Rim International Conference on artificial Intelligence (PRICAI) / 15th Pacific Rim Knowledge Acquisition Workshop (PKAW)

作者： Lu, Jiaxin Zhou, Xinyu Ma, Yong Wang, Mingwen Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China

ISBN: (纸本)9783319973104;9783319973098

artificial bee colony (ABC) algorithm is a powerful global optimization method. For some complex optimization problems, however, ABC also suffers from a slow convergence speed due to its solution search equation which has strong exploration ability but poor exploitation ability. To solve the defect, in this paper, we proposed an elite group guided ABC algorithm with a modified neighborhood search operator, which aims to utilize the valuable information of elite individuals to guide search. First, some food sources with good fitness values are chosen as the elite individuals, and then they are used to construct an elite group. Second, in the onlooker bee phase, a novel solution search equation is designed based on the elite group, which introduces a parameter MR (modification rate) to control the greediness degree of the elite group guidance. Last, a modified neighborhood search operator is proposed based on the elite group, which is exploited to produce fine search in the vicinity of the elite individuals for a better tradeoff between exploration and exploitation abilities. In the experiments, 22 well-known test functions were used. The experimental results compared with other five ABC variants showed that our approach can achieve better or at least comparable performance on most of the test functions.

关键词： artificial bee colony algorithm Solution search equation Elite group Neighborhood search

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An Adaptive Unified artificial bee colony algorithm for Global Optimization 30

An Adaptive Unified Artificial Bee Colony Algorithm for Glob...

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30th Chinese Control and Decision Conference (CCDC)

作者： Yang, Yang Xu, Feiyi Hu, Haidong Gao, Hao Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing Peoples R China Beijing Inst Control Engn Beijing Peoples R China

ISBN: (纸本)9781538612446

The artificial bee colony (ABC) algorithm invented by Karaboga is a relative new nature inspired heuristic for optimization problem. It has been proved to be competitive with some conventional optimization algorithms. This paper proposes an adaptive unified artificial bee colony (auABC) algorithm which employs a single equation unifying multiple strategies into one expression. As we all know, ABC is good at exploration but poor at exploitation due to the insufficiency in its solution search equation and its one-dimensional search strategy leads to slower convergence speed. In order to improve the above defects, we created a combined self-adaptive equation that its parameters are determined by the current iteration times to solve the problem mentioned above. We also introduce a chaotic strategy when generating candidate food sources which balance the proportion of exploration and exploitation. Experiments conducted on benchmark functions demonstrate that our algorithm achieves good performance in both unimodal and multimodal functions as expected compared to several state-of-the-art algorithms.

关键词： artificial bee colony algorithm Solution search equation Adaptive strategy

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Modified Naive Bayes algorithm for Network Intrusion Detection based on artificial bee colony algorithm 4

Modified Naive Bayes Algorithm for Network Intrusion Detecti...

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4th IEEE International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS)

作者： Yang, Juan Ye, Zhiwei Yan, Lingyu Gu, Wei Wang, Ruoxi Hubei Univ Technol Sch Comp Sci Wuhan 430068 Hubei Peoples R China Wuhan FiberHome Tech Serv Co Ltd Wuhan Hubei Peoples R China

ISBN: (纸本)9781538675878

Intrusion detection algorithm based on machine learning is a research hotspot in network security detection. The diversity of network intrusion detection data sets is one of the major factors that affect the practical application of machine learning. Therefore, some swarm intelligence algorithms were utilized to optimize parameters of machine learning methods for feature selection or feature weight in network intrusion. In this paper, a modified Naive Bayes algorithm based on artificial bee colony algorithm (ABCWNB, in brief) is proposed. The proposed method is tested on two public data sets and NSL-KDD data sets. Experimental results show that compared to Naive Bayes classifier based on genetic algorithm (GAWNB), Naive Bayes classifier based on grey wolf optimizer (GWOWNB), Naive Bayes classifier based on water wave optimization (WWOWNB) and basic Naive Bayes classifier, the proposed method can effectively improve the network intrusion detection rate, which can well detect various types of network intrusion and greatly improve the security performance of the network.

关键词： Naive Bayes feature weight artificial bee colony algorithm intrusion detection

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Adaptive artificial bee colony algorithm Based on Orthogonal Design in Path Design 3

Adaptive Artificial Bee Colony Algorithm Based on Orthogonal...

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3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Xu Shijie Shandong Lab Vocat & Tech Coll Jinan Shandong Peoples R China

ISBN: (纸本)9781538645093

Based on artificial colony algorithm slow convergence speed in the process of operation, easy to appear stagnation phenomenon, etc, this paper proposes a adaptive artificial colony algorithm based on orthogonal design, on the basis of artificial colony algorithm, at the same time by orthogonal design, the orthogonal discrete solution space, adaptive adjustment of step length, improve the performance of the algorithm, is more advantageous to global optimization, convergence speed had the very big enhancement. The simulation results show that the algorithm has a better global optimal search ability and the convergence speed is greatly improved. The improved artificial swarm optimization algorithm solves the problem of group path optimization and provides a unique method for path design.

关键词： artificial bee colony algorithm path design orthogonal design combinatorial optimization

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Study of Modified Chaotic artificial bee colony algorithm Based on Process Solutions

Study of Modified Chaotic Artificial Bee Colony Algorithm Ba...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Xueli Jiang Fenmei Wang Jingang Jiang Chinese Academy of Sciences Hefei Institutes of Physical Science Hefei China Army Academy of Artillery and Air Defence Hefei 230036

ISBN: (纸本)9781728125992

artificial bee colony (ABC) has been wildly used in various global optimization problems. In order to avoid premature convergence, a modified chaotic artificial bee colony algorithm using process solutions (CABC-WTB) is presented. Chaos initialization increases population diversity, chaotic local search helps to get out of local minima, process solutions are used to improve convergence speed. Comparative studies have been performed using 800 randomly generated functions. The proposed algorithm raises the performance of ABC algorithm.

关键词： Global optimization artificial bee colony algorithm Chaos theory Process solutions Global optimization artificial bee colony algorithm Chaos theory SOLUTION PROCESSING activity based costing Solutions Converge

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Real-time Dynamic Track Planning of Multi-UAV Formation Based on Improved artificial bee colony algorithm 37

Real-time Dynamic Track Planning of Multi-UAV Formation Base...

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37th Chinese Control Conference (CCC)

作者： Tian, Guozun Zhang, Lan Bai, Xin Wang, Bing Univ Sci & Technol Sch Automat Elect Engn Beijing 100083 Peoples R China Tianjin Zhongwei Aerosp Data Syst Technol Co Ltd Tianjin 300301 Peoples R China

ISBN: (纸本)9789881563958

Aiming at the Quad-rotor UAV formation in the environment of dynamic threats, improved artificial bee colony (IABC) algorithm was suggested in the real-time track planning because of its fewer control parameters and faster convergence. The simulation results show that the track planning based on IABC algorithm is faster than that based on ant colony optimization (ACO) algorithm in avoiding threats, and the real-time dynamic track planning based on IABC is effective in scenarios of the fixed and unfixed multi-UAV formation faced with dynamic threats.

关键词： multi-UAV formation dynamic track planning artificial bee colony algorithm

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