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An Improved artificial bee colony algorithm for Model-Free Active Noise Control: algorithm and Implementation

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： Ren, Xing Zhang, Hongwei Southwest Jiaotong Univ Sch Elect Engn Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 611756 Sichuan Peoples R China Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Guangdong Peoples R China

Active noise control (ANC) usually adopts the filtered-x least mean square (FxLMS) algorithm. However, the FxLMS algorithm requires the identification of the secondary path. Problems of time-varying secondary path and imperfect secondary path modeling require model-free ANC algorithms for practical applications. In this article, the artificial bee colony (ABC) algorithm is improved to suitably develop a novel ANC algorithm without secondary path modeling. In addition, the FxLMS algorithm may fall into local minima, while our proposed algorithm features its global optimization ability. In order to have anti-interference ability, in our algorithm, a forgetting factor is introduced into the fitness function. Moreover, the least mean square (LMS) algorithm is integrated into the ABC algorithm to enhance its exploitation ability, further accelerate the convergence rate, and improve the noise reduction performance. Compared with some closely related population-based model-free ANC algorithms, our proposed algorithm has anti-interference ability, faster convergence rate, and better noise reduction performance. Simulations and experiments are conducted to illustrate the effectiveness of the proposed algorithm.

关键词： artificial bee colony algorithm Microphones Optimization Convergence Loudspeakers Transfer functions Noise reduction Active noise control (ANC) artificial bee colony (ABC) least mean square (LMS)

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Enhancing artificial bee colony algorithm using more information-based search equations

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INFORMATION SCIENCES 2014年 270卷 112-133页

作者： Gao, Wei-feng Liu, San-yang Huang, Ling-ling China Univ Petr Sch Sci Qingdao 266580 Peoples R China Xidian Univ Sch Sci Xian 710071 Shannxi Peoples R China

artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, in this paper, we propose a novel ABC method called as EABC to improve the performance of ABC. In our method, in order to balance the exploration and the exploitation, two new search equations are presented to generate candidate solutions in the employed bee phase and the onlookers phase, respectively. Additionally, we use a more robust calculation to determine and compare the quality of alternative solutions. Experiments are conducted on a set of 48 benchmark functions and also two engineering optimization problems. The results show that EABC significantly improves the performance of ABC, offering faster global convergence, higher solution quality, and stronger robustness when compared with the other algorithms. (c) 2014 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Evolutionary algorithms Search equation Differential evolution

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A comparative study of artificial bee colony algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2009年第1期214卷 108-132页

作者： Karaboga, Dervis Akay, Bahriye Erciyes Univ Dept Comp Engn TR-38039 Kayseri Turkey

artificial bee colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Swarm intelligence Evolution strategies Genetic algorithms Differential evolution Particle swarm optimization artificial bee colony algorithm Unconstrained optimization

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A Hybrid Discrete artificial bee colony algorithm for Imaging Satellite Mission Planning

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IEEE ACCESS 2023年 11卷 40006-40017页

作者： Yang, Yang Liu, Desheng Space Engn Univ Sci & Technol Complex Elect Syst Simulat Lab Beijing 101400 Peoples R China

Imaging satellite mission planning has received more and more attention as one of the core problems in the field of imaging satellite applications. In this paper, a hybrid discrete artificial bee colony (HDABC) algorithm is proposed to address this problem. The HDABC algorithm improves the three search phases of the basic artificial bee colony (ABC) algorithm to make them applicable to the discrete satellite mission planning problem. In the employed bee search phase, the population is divided and a multi-strategy search equation mechanism is used to balance the exploration and development of the algorithm. In the following bee search phase, two kinds of neighborhood search operators are designed based on the problem characteristics to further improve the fitness values of the better solutions. In the scout bee search phase, a migration operator and an immigration operator are introduced to improve the fitness values of the worse solutions and promote the exchange of different subpopulations to achieve co-evolution. In the experimental part, orthogonal experimental design is used to determine the appropriate algorithm parameters. Simulation experiments are carried out to test problems of different sizes. The experimental results show that the proposed HDABC algorithm shows good performance.

关键词： Satellites Planning Heuristic algorithms Optimization Imaging artificial bee colony algorithm Search problems imaging satellite mission planning

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Integrating artificial bee colony algorithm and BP Neural Network for Software Aging Prediction in IoT Environment

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IEEE ACCESS 2019年 7卷 32941-32948页

作者： Liu, Jing Meng, Lingze Inner Mongolia Univ Coll Comp Sci Hohhot 010021 Peoples R China

Software aging is a common phenomenon that exists in systems that require long periods of operation, especially in Internet-of-Things environments. The back propagation (BP) neural network has been adopted widely to predict the trend of software aging. However, the weight and threshold of the BP neural network are randomly initialized, so it is easy to get the unsatisfactory local optimal solutions and the convergence speed of computing is slow. In this paper, we propose a novel software aging prediction method using the artificial bee colony algorithm to optimize the BP neural network model for achieving better software aging prediction accuracy. The experiment results show that our method fits the prediction trend of software aging more accurately than the traditional BP neural network, and our method also has faster convergence speed and more stable prediction results.

关键词： artificial bee colony algorithm BP neural network software aging prediction accuracy

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Economic Maintenance Planning of Complex Systems Based on Discrete artificial bee colony algorithm

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IEEE ACCESS 2020年 8卷 108062-108071页

作者： Chen, Zhaofang Zhang, Lele Tian, Guangdong Nasr, Emad Abouel Shandong Univ Sch Mech Engn Jinan 250061 Peoples R China Fujian Univ Technol Management Coll Fuzhou 350118 Peoples R China Jilin Univ Transportat Coll Changchun 130022 Peoples R China King Saud Univ Ind Engn Dept Coll Engn Riyadh 11421 Saudi Arabia Helwan Univ Fac Engn Mech Engn Dept Cairo 11732 Egypt

In recent years, system maintenance has become a hot topic that has attracted significant research interests from both academia and industry, and has found applications in many areas. A modern system usually consists of many components, among which there may be different dependencies. The maintenance strategy that ignores the dependencies among components cannot meet the actual engineering requirements and may even affect the usability of the entire system. Therefore, in this paper, we study the economic dependence of equipment cost and duration sharing when components are replaced at the same time. By characterizing the relationship between the effective service age and reliability of components using the Weibull distribution, we establish a selective maintenance model of multi-state complex system with economic dependence. The discrete artificial bee algorithm is used as the global optimization method to solve the dual- objective optimization model and obtain the Pareto solution set of the maintenance level of each component. A case study of the Shooman network system is performed to demonstrate the usefulness of the model and the importance of considering the economic dependencies between components in maintenance decisions.

关键词： Maintenance engineering Economics Complex systems Reliability Planning Weibull distribution artificial bee colony algorithm Selective maintenance multi-state systems economic dependence Weibull distribution artificial bee colony

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Multi-objective artificial bee colony algorithm for simultaneous sequencing and balancing of mixed model assembly line

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第9-12期75卷 1809-1827页

作者： Saif, Ullah Guan, Zailin Liu, Weiqi Wang, Baoxi Zhang, Chaoyong Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol HUST SANY Joint Lab Adv Mfg Technol Wuhan 430074 Hunan Peoples R China Univ Engn & Technol Dept Ind Engn Taxila Pakistan

In recent years, mixed model assembly lines are gaining popularity to produce a variety of models on the single-model assembly lines. Mixed model assembly lines have two types of problems which include sequencing of different models on the line and balancing of assembly line. These two problems collectively affect the performance of assembly lines, and therefore, current research is aimed to balance the workload of different models on each station, to reduce the deviation of workload of a station from the average workload of all the stations and to minimize the total flow time of models on different stations simultaneously. A multi-objective artificial bee colony (multi-ABC) algorithm for simultaneous sequencing and balancing problem with Pareto concepts and local search mechanism is presented. Two kinds of mixed model assembly line problems are analysed. For the first and second problems, each model task time data and precedence relation data are taken from standard assembly line problems, from operation research library (ORL) and from a truck manufacturing company in China, respectively. Both problems are solved using the proposed multi-ABC algorithm on two different demand scenarios of models, and the results are compared against the results obtained from a famous algorithm in the literature, i.e. non-dominated sorting genetic algorithm (NSGA) II. Computational results of the selected problems indicate that the proposed multi-ABC algorithm outperforms NSGA II and gives better Pareto solutions for the selected problems on different demand scenarios of models.

关键词： Mixed model assembly line Simultaneous sequencing and balancing Pareto solutions artificial bee colony algorithm

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Parameter Estimation for Chaotic Systems by Improved artificial bee colony algorithm

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2013年第3期10卷 756-762页

作者： Li, Xiangtao Yin, Minghao NE Normal Univ Coll Comp Sci Changchun 130117 Peoples R China

This paper is concerned with the parameter estimation of nonlinear chaotic system, which could be essentially formulated as a multi-dimension optimization problem. In this article, an improved artificial bee colony algorithm is implemented to solve parameter estimation for chaotic systems. This algorithm can combine the stochastic exploration of the artificial bee colony and the exploitation capability of new search strategies. Experiments have been conducted on Lorenz system and Chen system. The proposed algorithm is applied to estimate the parameters of these two systems. Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to drift particle swarm optimization, particle swarm optimization and genetic algorithm from literature when considering the quality of the solutions obtained.

关键词： artificial bee colony algorithm Exploration Exploitation Chaotic System Parameter Estimation Improved algorithm

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An Efficient artificial bee colony algorithm With an Improved Linkage Identification Method

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第6期52卷 4400-4414页

作者： Gao, Hao Fu, Zheng Pun, Chi-Man Zhang, Jun Kwong, Sam Nanjing Univ Posts & Telecommun Coll Automat Coll Artificial Intelligence Nanjing 210023 Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Victoria Univ Inst Sustainable Ind & Liveable Cities Coll Engn & Sci Melbourne Vic 8001 Australia City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Hong Kong Peoples R China

The artificial colony (ABC) algorithm shows a relatively powerful exploration search capability but is constrained by the curse of dimensionality, especially on nonseparable functions, where its convergence speed slows dramatically. In this article, based on an analysis of the difference between updating mechanisms that include both all-variable and one-variable updating mechanisms, we find that when equipped with the former strategy, the algorithm rapidly converges to an optimal region, while with the latter strategy, it searches the solution space thoroughly. To utilize multivariable and one-variable updating mechanisms on nonseparable and separable functions, respectively, we embed an improved linkage identification strategy into the ABC by detecting the linkage between variables more effectively. Then, we propose three common strategies for ABC to improve its performance. First, a new approach that considers the historic experiences of the population is proposed to balance exploration and exploitation. Second, a new strategy for initializing scout bees is used to reduce the number of function evaluations. Finally, the individual with the worst performance is updated with a defined probability on multiple dimensions instead of one dimension, causing it to follow the population steps on nonseparable functions. This article is the first to propose all these concepts, which could be adopted for other ABC variants. The effectiveness of our algorithm is validated through basic, CEC2010, CEC2013, and CEC2014 functions and real-world problems.

关键词： Couplings Sociology Statistics artificial bee colony algorithm Iron Convergence Mathematical model artificial bee colony (ABC) economic dispatch problem historic experiences linkage identification strategy (LIS) nonseparable functions scout bees truss structure problem

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An evacuation simulation method based on an improved artificial bee colony algorithm and a social force model

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APPLIED INTELLIGENCE 2021年第1期51卷 100-123页

作者： Zhao, Yuan Liu, Hong Gao, Kaizhou Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China Shandong Prov Key Lab Distributed Comp Software N Jinan 250014 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China

Simulation modeling is an important tool for simulating crowd behavior and studying the law of crowd evacuation. It is of great significance for exploring evacuation management methods in emergency situations. The real-time change of evacuation is the main challenge of simulation modeling. In the evacuation simulation, it is difficult for people to choose a suitable route according to the change of evacuation dynamics. This paper proposes a new evacuation simulation method which combines an improved artificial bee colony algorithm for dynamic path planning and SFM (Social Force Model) for simulating the movement of pedestrians, to providing pedestrians with timely route selection. In the path planning layer, we developed a MABCM (Multiple-subpopulations artificial bee colony with Memory) algorithm and proposed a new exit evaluation strategy. These methods can plan a route with the shortest evacuation time for pedestrians according to the dynamic changes of evacuation and improve evacuation efficiency. In the simulated motion layer, we use the SFM to avoid collisions and achieve the reproduction of the evacuation scene. We verified the performance of the proposed MABCM on the CEC 2014 benchmark suite, and the results show that it is superior to the four existing artificial bee colony algorithms in most cases. The proposed crowd evacuation method is verified on an existing SFM platform. The experimental results indicate that the proposed method can efficiently evacuate a dense crowd in multiple scenes and can effectively shorten evacuation time.

关键词： artificial bee colony algorithm Crowd evacuation Computer simulation Swarm intelligence algorithm

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