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A course control system of unmanned surface vehicle (USV) using back-propagation neural network (BPNN) and artificial bee colony (ABC) algorithm 8th

A course control system of unmanned surface vehicle (USV) us...

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8th International Conference on Advances in Information Technology (IAIT)

作者： Fang, Yang Pang, Muye Wang, Biao Wuhan Univ Technol Sch Automa Wuhan 430070 Hubei Peoples R China

Course control is the basis of Unmanned Surface Vehicle (USV) control system, largely determines the performance of USV. Because of some uncontrollable factors such as wind, wave and other random disturbances, the course control of an Unmanned Surface Vehicle (USV) is always difficult. In recent years, there have been some studies of adaptive course control system for USV, but they are not precise enough for engineering practice. In this paper, we propose a novel adaptive course control method based on Back-propagation Neural Network (BPNN) and artificial bee colony (ABC) algorithm. We use classic PID algorithm as the main course control algorithm, and back-propagation neural network (BPNN) is also utilized to achieve more effective self-adaptive PID control. At the same time, in order to improve the convergence speed and precision of BPNN, we bring in artificial bee colony (ABC) algorithm to minimize the error of system and adjust the weight of BPNN. The system has been proven in simulation that it can accurately output the rudder angle according to the input course angle and can perform better than that without ABC algorithm optimization. The error of parameters obtained by this method is within the acceptable range, which can provide reference for engineering practice. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Course control system Back-propagation Neural Network artificial bee colony algorithm PID algorithm

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artificial bee colony algorithm Based on K-means Clustering for Droplet Property Optimization 1

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8th International Conference on Intelligent Robotics and Applications (ICIRA)

作者： Sun, Liling Hu, Jingtao He, Maowei Chen, Hanning Chinese Acad Sci Shenyang Inst Automat Dept Informat Serv & Intelligent Control Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100040 Peoples R China Tianjin Polytech Univ Sch Comp Sci & Software Tianjin 300387 Peoples R China

ISBN: (数字)9783319228792

ISBN: (纸本)9783319228792;9783319228785

The major challenge in printable electronics fabrication is to effectively and accurately control a drop-on-demand (DoD) inkjet printhead for high printing quality. In this paper, a prediction model based on Lumped Element Modeling (LEM) is proposed to search the parameters of driving waveform for obtaining the desired droplet properties. Although the evolution algorithms are helpful to solve this problem, the classical evolution algorithms may get trapped into local optimal due to the inefficiency of local search. To overcome it, we present an improved artificial bee colony algorithm based on K-means clustering (KCABC), which enhances the population diversity by dynamically clustering and increases the convergence rates by the modification of information communication in the employed bees' phase. Combined with KCABC, the prediction model is applied to optimize the droplet volume and velocity of nanosilver ink for high printing quality. Experimental results demonstrate the proposed prediction model with KCABC plays a good performance in terms of efficiency and accuracy of searching the appropriate combination of waveform parameters for printable electronics fabrication.

关键词： Piezoelectric inkjet system Lumped element modeling artificial bee colony algorithm Swarm intelligent algorithm

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A Multi-Objective artificial bee colony algorithm Combined with a Local Search Method

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INTERNATIONAL JOURNAL ON artificial INTELLIGENCE TOOLS 2016年第3期25卷 1650009-1650009页

作者： Tang, Langping Zhou, Yuren Xiang, Yi Lai, Xinsheng South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Collaborat Innovat Ctr High Performance Comp Guangzhou 510006 Guangdong Peoples R China Shangrao Normal Univ Sch Math & Comp Sci Shangrao 334001 Peoples R China

This paper proposes a new multi-objective artificial bee colony (ABC) algorithm called MOABCLS by combining ABC with a polynomial mutation based local search method. In this algorithm, an external archive is used to store the non-dominated solutions found so far which are maintained by the crowding distance method. A global best food source gbest is selected and used to produce new food sources in both employed and onlooker bee phases. The aim of adopting a local search is to keep good balance between exploration and exploitation. The MOABCLS is able to deal with both unconstrained and constrained problems, and it is evaluated on test functions (with up to five objectives) taken from the CEC09 competition. The performance of MOABCLS is compared with that of eight state-of-the-art multi-objective algorithms with respect to IGD metric. It is shown by the Wilcoxon test results that MOABCLS performs competitively or even better than the peer algorithms. Further experimental results clearly demonstrate MOABCLS's ability of finding a set of well converged and appropriately distributed non-dominated solutions, and the performance promotion by introducing the local search method.

关键词： Multi-objective optimization artificial bee colony algorithm local search method CEC09 test functions

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A wavelet thresholding method for vibration signals denoising of high-piled wharf structure based on a modified artificial bee colony algorithm

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JOURNAL OF VIBROENGINEERING 2016年第5期18卷 2899-2915页

作者： Zhang, Xun Li, Juelong Xing, Jianchun Wang, Ping Xie, Liqiang PLA Univ Sci & Technol Coll Def Engn Nanjing 210007 Jiangsu Peoples R China Res Ctr Coastal Def Engn Beijing 100841 Peoples R China

Vibration monitoring signals are widely used for damage alarming among the structural health monitoring system. However, these signals are easily corrupted by the environmental noise in the collecting that hampers the accuracy and reliability of measured results. In this paper, a modified artificial bee colony (MABC) algorithm-based wavelet thresholding method has been proposed for noise reduction in the real measured vibration signals. Kent chaotic map and general opposition-based learning strategies are firstly adopted to initialize the colony. Tournament selection mechanism is then employed to choose the food source. Finally, the Kent chaotic search is applied to exploit the global optimum solution according to the current optimal value. Moreover, a generalized cross validation (GCV) based fitness function is constructed without requiring foreknowledge of the noise-free signals. A physical model experiment for a high-piled wharf structure is implemented to verify the feasibility of the proposed signal denoising approach. Particle swarm optimization (PSO) algorithm, basic artificial bee colony (BABC) algorithm and Logistic chaos artificial bee colony (LABC) algorithm and are also taken as contrast tests. Comparison results demonstrate that the proposed algorithm outperforms the other algorithms in terms of convergence speed and precision, and can effectively reduce the noise from the measured vibration signals of the high-piled wharf structure.

关键词： high-piled wharf vibration signal wavelet thresholding denoising artificial bee colony algorithm

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Hybrid artificial bee colony algorithm with Differential Evolution and Free Search for Numerical Function Optimization

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INTERNATIONAL JOURNAL ON artificial INTELLIGENCE TOOLS 2016年第4期25卷 1650020-1650020页

作者： Lian Lian Fu Zaifeng Yang Guangfei Huang Yi Dalian Univ Technol Sch Transportat & Logist Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Fac Econ & Management Dalian 116024 Liaoning Peoples R China

artificial bee colony (ABC) algorithm invented by Karaboga has been proved to be an efficient technique compared with other biological-inspired algorithms for solving numerical optimization problems. Unfortunately, convergence speed of ABC is slow when working with certain optimization problems and some complex multimodal problems. Aiming at the shortcomings, a hybrid artificial bee colony algorithm is proposed in this paper. In the hybrid ABC, an improved search operator learned from Differential Evolution (DE) is applied to enhance search process, and a not-so-good solutions selection strategy inspired by free search algorithm (FS) is introduced to avoid local optimum. Especially, a reverse selection strategy is also employed to do improvement in onlooker bee phase. In addition, chaotic systems based on the tent map are executed in population initialization and scout bee's phase. The proposed algorithm is conducted on a set of 40 optimization test functions with different mathematical characteristics. The numerical results of the data analysis, statistical analysis, robustness analysis and the comparisons with other state-of-the-art-algorithms demonstrate that the proposed hybrid ABC algorithm provides excellent convergence and global search ability.

关键词： artificial bee colony algorithm differential evolution free search selection chaotic systems numerical function optimization

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An Efficient Method to Generate Test Data for Software Structural Testing Using artificial bee colony Optimization algorithm

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2017年第6期27卷 951-966页

作者： Aghdam, Zohreh Karimi Arasteh, Bahman Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran

Software testing is a process for determining the quality of software system. Many small and medium-sized software projects can be manually tested. Nevertheless, due to the widespread extension of software in large-scale projects, testing them will be highly time consuming and costly. Hence, automated software testing (AST) is considered to be as a solution which can ease and simplify heavy and cumbersome tasks involved in software testing. For AST, certain data are needed through which the quality of systems can be evaluated. In this paper, an artificial bee colony (ABC) algorithm was used for solving the issue of test data generation and branch coverage criterion was used as a fitness function for optimizing the proposed solutions. For doing comparisons, seven well-known and traditional programs in the literature were used as benchmarks. The experimental results indicate that our method, on average, outperforms simulated annealing, genetic algorithm, particle swarm optimization and ant colony optimization based on the following four criteria: 99.99% average branch coverage, 99.94% success rate, 3.59 average convergence generation and 0.18 ms average execution time.

关键词： Software testing automated test data generation artificial bee colony algorithm branch coverage

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A course control system of unmanned surface vehicle (USV) using back-propagation neural network (BPNN) and artificial bee colony (ABC) algorithm

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Procedia Computer Science 2017年 111卷 361-366页

作者： Yang Fang Muye Pang Biao Wang School of Automation Wuhan University of technology Wuhan 430070 China

关键词： Course control system Back-propagation Neural Network artificial bee colony algorithm PID algorithm

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Weighted Global artificial bee colony algorithm Makes Gas Sensor Deployment Efficient

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SENSORS 2016年第6期16卷 888-888页

作者： Jiang, Ye He, Ziqing Li, Yanhai Xu, Zhengyi Wei, Jianming Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This paper proposes an improved artificial bee colony algorithm named Weighted Global ABC (WGABC) algorithm, which is designed to improve the convergence speed in the search stage of solution search equation. The new method not only considers the effect of global factors on the convergence speed in the search phase, but also provides the expression of global factor weights. Experiment on benchmark functions proved that the algorithm can improve the convergence speed greatly. We arrive at the gas diffusion concentration based on the theory of CFD and then simulate the gas diffusion model with the influence of buildings based on the algorithm. Simulation verified the effectiveness of the WGABC algorithm in improving the convergence speed in optimal deployment scheme of gas sensors. Finally, it is verified that the optimal deployment method based on WGABC algorithm can improve the monitoring efficiency of sensors greatly as compared with the conventional deployment methods.

关键词： artificial bee colony algorithm global factor convergence speed benchmark function gas diffusion model optimal deployment

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Best Supported Emigrant Creation For Parallel Implementation Of artificial bee colony algorithm

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ISTANBUL UNIVERSITY-JOURNAL OF ELECTRICAL AND ELECTRONICS ENGINEERING 2016年第2期16卷 2055-2064页

作者： Karaboga, Dervis Aslan, Selcuk Erciyes Univ Dept Comp Engn Kayeri Turkey Ondokuz Mayis Univ Dept Comp Engn Samsun Turkey

artificial bee colony algorithm inspired by the foraging behavior of real honey bees is one of the most popular swarm intelligence based optimization techniques. Like other population based evolutionary computation approaches, artificial bee colony algorithm is intrinsically suitable for distributed architectures. However, determining which food source should be chosen to distribute between subcolonies and communication topology applied still remain as an important problem for parallel implementations. In this study, a new schema for increasing the quality of the distributed source by changing best solution is presented. The proposed model is adapted to ring migration topology and its effectivenes is compared with conventional ring based topology in which best food sources in each subpopulation are distributed and the original sequential counterpart. Comparative results show that the proposed model increased the quality of the solutions and early convergence speed while protecting the speedup gain.

关键词： artificial bee colony algorithm parallelization emigrant creation

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An improved artificial bee colony algorithm for vehicle routing problem with time windows: A real case in Dalian

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ADVANCES IN MECHANICAL ENGINEERING 2016年第8期8卷 1687814016665298-1687814016665298页

作者： Yu, Shaoqiang Tai, Cuicui Liu, Yanan Gao, Linjie Dalian Maritime Univ Transportat Management Coll Dalian Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China

This study has been motivated from a real western-style food delivery problem in Dalian city, China, which can be described as a vehicle routing problem with time windows. An integer linear model for the problem is developed, and an improved artificial bee colony algorithm, which possesses a new strategy called an adaptive strategy, a crossover operation, and a mutation operation, is proposed to solve the problem. Then, the effectiveness of the proposed improved artificial bee colony is first validated by some benchmark instances. Furthermore, results obtained on a real-case instance for western-style food delivery problem in Dalian city are also discussed. In this case, the results indicate that the improved artificial bee colony algorithm is a feasible method to solve the real vehicle routing problem with time windows such as western-style food delivery.

关键词： artificial bee colony algorithm adaptive strategy crossover operation mutation operation vehicle routing problem

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