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A hybrid of real coded genetic algorithm and artificial fish swarm algorithm for short-term optimal hydrothermal scheduling

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 62卷 617-629页

作者： Fang, Na Zhou, Jianzhong Zhang, Rui Liu, Yi Zhang, Yongchuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Peoples R China

The short-term hydrothermal scheduling (SHS) is a complicated nonlinear optimization problem with a series of hydraulic and electric system constraints. This paper presents a hybrid algorithm for solving SHS problem by combining real coded genetic algorithm and artificial fish swarm algorithm (RCGA-AFSA), which takes advantage of their complementary ability of global and local search for optimal solution. Real coded genetic algorithm (RCGA) is applied as global search, which can explore more promising solution spaces and give a good direction to the global optimal region. artificial fish swarm algorithm (AFSA) is used as local search to obtain the final optimal solution for improving the exploitation capability of algorithm. The water transport delay between connected reservoirs is taken into account in this paper. Moreover, new coarse and fine adjustment methods without any penalty factors and extra parameters are proposed to deal with all equality and inequality constraints. To verify the feasibility and effectiveness of RCGA-AFSA, the proposed method is tested on two hydrothermal systems. Compared with other methods reported in the literature, the simulation results obtained by hybrid RCGA-AFSA are superior in fuel cost and computation time. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Short-term hydrothermal scheduling Real coded genetic algorithm artificial fish swarm algorithm Constraints handling

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Application of an artificial fish swarm algorithm in Solving Multiobjective Trajectory Optimization Problems

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CHEMISTRY AND TECHNOLOGY OF FUELS AND OILS 2017年第4期53卷 541-547页

作者： Sun, Tengfei Zhang, Hui Liu, Shujie Cao, Yanfeng CNOOC Res Inst Beijing Peoples R China China Univ Petr Beijing Peoples R China

Drilling faces many complex design and multiobjective optimization problems. Solving these problems is also a critical and complicated part of drilling optimization as part of well trajectory design and optimization. Many researchers have developed many algorithms, but they have some disadvantages. We take the shortest total borehole length, the highest target shooting accuracy, the lowest cost, and the minimum friction as the multiobjective function, and we use a fish swarm algorithm for trajectory optimization. In this paper, we present the idea of using a nondominant relation for sorting in the algorithm and we also use an optimization program in the Matlab software to obtain all numerical solutions satisfying the constraints. Therefore it is quite adaptable for introducing the idea of nondominant sorting into appropriate multiobjective optimization problems based on a fish swarm algorithm. We give an example of the calculation, and also show that the algorithm and the calculation procedure are accurate and reliable. The algorithm has a simple structure, a small number of calculations, and good convergence.

关键词： artificial fish swarm algorithm sorting trajectory multiobjective optimization

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A fault detection optimization method based on chaos adaptive artificial fish swarm algorithm on distributed control system

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2018年第9期232卷 1182-1193页

作者： Li, Teng-Hui Xie, Shou-Sheng Liu, Sheng-Ping Xiao, Lei Jia, Wei-Zhou He, Da-Wei Air Force Engn Univ Aeronaut & Astronaut Engn Inst Xian 710038 Shanxi Peoples R China

For the purpose of improving fault detection accuracy of aero-engine distributed control system, an optimal design method based on chaos adaptive artificial fish swarm algorithm for distributed control system fault detection observer was proposed. First, in order to better simulate the actual distributed control system, dual channel multiple packet transmission was converted to switching system and short time-varying delay was modeled as an uncertainty in system models. Then, a fault observer is designed to obtain the state-space model of error system and the chaos adaptive artificial fish swarm algorithm is introduced. Furthermore, the ratio value of the residual signal transfer function, respectively, to the unknown disturbance signal and fault signal, are regarded as the objective function, and the minimum of the objective function is optimized to get the optimal observer matrix by chaos adaptive artificial fish swarm algorithm with the constraint of error system stability. Finally, the structure and operating principle of aero-engine distributed control system semi-physical platform is introduced, and comparison is made between the simulation of fault detection optimization method and the tradition robust fault detection method on the platform. The simulation results verify that the proposed method can efficiently reduce fault positive ratio and fault negative ratio simultaneously, which can expand the selection scope of threshold value and improve accuracy of fault detection.

关键词： Fault detection optimization switching system artificial fish swarm algorithm chaos semi-physical platform

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Mechanical parameter inversion in tunnel engineering using support vector regression optimized by multi-strategy artificial fish swarm algorithm

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2019年 83卷 425-436页

作者： Zhuang, D. Y. Ma, K. Tang, C. A. Liang, Z. Z. Wang, K. K. Wang, Z. W. Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Inst Rock Instabil & Seism Res Dalian 116024 Liaoning Peoples R China China Coal Res Inst Mine Safety Technol Branch Beijing 100013 Peoples R China

Fast and efficient determination of the mechanical parameters of surrounding rock masses is vitally important to the calculation and evaluation of the stability of surrounding rock masses in tunnel engineering. In this paper, a displacement back-analysis (DBA) model is proposed to identify the mechanical parameters based on support vector regression (SVR) optimized by multi-strategy artificial fish swarm algorithm (MAFSA). The MAFSA adopts the differential evolution strategy, the particle swarm optimization strategy, the adaptive step size and phased vision strategy on the basis of artificial fish swarm algorithm (AFSA) to enhance the global search capability and improve convergence speed and optimization accuracy. Then, the kernel width and the penalty parameter of SVR are optimized by MAFSA, forming into MAFSA-SVR. Meanwhile, the training and testing samples for MAFSA-SVR are constructed by orthogonal design and forward calculation by FLAC(3D) code. Finally, the DBA model is established based on MAFSA-SVR and applied to the mechanical parameter inversion of surrounding rock masses in the Heshi tunnel with the following conclusion: the relative errors of all the mechanical parameters are less than 8% between the inversed values of the DBA model based on MAFSA-SVR and the actual values. The method proposed in this paper could provide an efficient tool for the mechanical parameter inversion of the tunnel surrounding rock masses.

关键词： Displacement back-analysis Support vector regression artificial fish swarm algorithm Orthogonal test Mechanical parameter inversion

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A New artificial fish swarm algorithm for the Multiple Knapsack Problem

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2014年第3期E97D卷 455-468页

作者： Liu, Qing Odaka, Tomohiro Kuroiwa, Jousuke Shirai, Haruhiko Ogura, Hisakazu Univ Fukui Fukui 9108501 Japan

A new artificial fish swarm algorithm (AFSA) for solving the multiple knapsack problem (MKP) is introduced in this paper. In the proposed AFSA, artificial fish (AF) individuals are only allowed to search the region near constraint boundaries of the problem to be solved. For this purpose, several behaviors to be performed by AF individuals, including escaping behavior, randomly moving behavior, preying behavior and following behavior, were specially designed. Exhaustive experiments were implemented in order to investigate the proposed AFSA's performance. The results demonstrated the proposed AFSA has the ability of finding high-quality solutions with very fast speed, as compared with some other versions of AFSA based on different constraint-handling methods. This study is also meaningful for solving other constrained problems.

关键词： artificial fish swarm algorithm multiple knapsack problem constraint boundary search region

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A novel artificial fish swarm algorithm for solving large-scale reliability-redundancy application problem

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ISA TRANSACTIONS 2015年 59卷 105-113页

作者： He, Qiang Hu, Xiangtao Ren, Hong Zhang, Hongqi Chongqing Univ Urban Construct & Environm Engn Chongqing 400044 Peoples R China CETC Res Inst 38 Hefei 230088 Peoples R China

A novel artificial fish swarm algorithm (NAFSA) is proposed for solving large-scale reliability-redundancy allocation problem (RAP). In NAFSA, the social behaviors of fish swarm are classified in three ways: foraging behavior, reproductive behavior, and random behavior. The foraging behavior designs two position-updating strategies. And, the selection and crossover operators are applied to define the reproductive ability of an artificial fish. For the random behavior, which is essentially a mutation strategy, the basic cloud generator is used as the mutation operator. Finally, numerical results of four benchmark problems and a large-scale RAP are reported and compared. NAFSA shows good performance in terms of computational accuracy and computational efficiency for large scale RAP. (C) 2015 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Reliability-redundancy allocation problem artificial fish swarm algorithm GA Cloud model

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Combining weak-link co-evolution binary artificial fish swarm algorithm and complementarity measure for ensemble pruning

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第2期35卷 1431-1444页

作者： Zhu, Xuhui Ni, Zhiwei Zhang, Gongrang Jin, Feifei Cheng, Meiying Li, Jingming Hefei Univ Technol Sch Management Hefei Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei Anhui Peoples R China Huzhou Univ Business Sch Huzhou Peoples R China Anhui Univ Finance & Econ Sch Management Sci & Engn Bengbu Peoples R China

Diversity and accuracy of classifiers are widely recognized to be two key factors for a successful ensemble. The increase of diversity among classifiers must lead to the decrease of the average accuracy of that, and vice verse. Therefore, finding a tradeoff between the diversity and the accuracy of classifiers can make the ensemble perform the best. Existing ensemble pruning approaches always find the tradeoff using diversity measures and heuristic algorithms separately. Those ensemble pruning approaches based on diversity measures, using different strategies, cannot exactly find the tradeoff;Those approaches based on heuristic algorithms cannot also exhaustively search for that. To address the issue, Combining Weak-link Co-evolution Binary artificial fish swarm algorithm and Complementarity measure for Ensemble Pruning (CWCBAFCEP) is proposed using a combination of the proposed Weak-link Co-evolution Binary artificial fish swarm algorithm (WCBAFSA) and COMplementarity measure (COM). First, the classifiers in a constructed initial pool of classifiers are pre-pruned using COM, which significantly reduce the computational complexity of ensemble pruning. Second, the final ensemble extracted from the remaining classifiers after pre-pruning can be efficiently achieved using the proposed WCBAFSA. Experimental results on 25 datasets from the UCI Machine Learning Repository demonstrate that CWCBAFCEP performs much better than the original ensemble and other state-of-the-art ensemble pruning approaches, and that its effectiveness and efficiency. It provides a new research idea for ensemble pruning.

关键词： artificial fish swarm algorithm weak-link co-evolution mechanism complementarity measure ensemble pruning

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Application of artificial fish swarm algorithm for Synchronous Selection of Wavelengths and Spectral Pretreatment Methods in Spectrometric Analysis of Beef Adulteration

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FOOD ANALYTICAL METHODS 2018年第8期11卷 2229-2236页

作者： Chen, Wei Feng, Yao-Ze Jia, Gui-Feng Zhao, Hai-Tao Huazhong Agr Univ Coll Engn 1 Shizishan St Wuhan 430070 Hubei Peoples R China Minist Agr China Key Lab Agr Equipment Midlower Yangtze River Wuhan Hubei Peoples R China

In this paper, we proposed artificial fish swarm algorithm (AF) for synchronous selection of wavelengths and pretreatment methods during quantification of beef adulteration with spoiled beef based on the analysis of ultraviolet (UV), visible and near-infrared (Vis-NIR), and UV-Vis-NIR spectra. The best partial least squares regression model was obtained when the wavelength-then-pretreatment scheme was adopted in the Vis-NIR range where 11 wavelengths were utilized to produce an RPD value of 2.68. The coefficients of determination R-2 and root mean squared errors of the model were 0.91 and 0.08 as well as 0.87 and 0.11 for calibration and prediction, respectively. It was demonstrated that AF was a useful tool for model optimization as compared with genetic algorithm. Moreover, the sequence for selecting wavelengths and spectral pretreatment methods had great influence in model performance and could be decided by try-out approach since the performance due to selection sequence was sensitive to spectral range and optimization algorithm employed. Nevertheless, wavelength-then-pretreatment scheme was preferred due to simpler model structure and reduced computation loads. It was shown that Vis-NIR spectroscopy was feasible in quantifying beef adulteration, and AF was an advance method for optimizing model performance. Moreover, AF could be expended in more studies for optimizing food quality and safety analysis.

关键词： artificial fish swarm algorithm Beef adulteration Homologous meat Spectral pretreatment method Wavelength selection

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Structural multi-damage identification based on strain energy and micro-search artificial fish swarm algorithm

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JOURNAL OF VIBROENGINEERING 2017年第5期19卷 3255-3270页

作者： Guo, Huiyong Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Minist Educ Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China

A two-stage damage detection method based on strain energy and micro-search artificial fish swarm algorithm (MSAFSA) is presented for solving structural multi-damage problem. First, structural modal strain energy and energy dissipation process are analyzed and an improved modal strain energy dissipation ration index (IMSEDRI) is proposed to preliminarily detect suspected damage elements. Then, artificial fish swarm algorithm (AFSA) is used to identify damage extent of suspected damage elements. In general, the search efficiency of a basic AFSA isn't very efficient for the search procedure. So, a micro-search artificial fish swarm algorithm is presented in this paper. The simulation results demonstrate that the damage detection method can estimate the damage locations and extent with good accuracy, and the calculated results of the proposed MSAFSA are obviously superior to those of both the basic AFSA and the AFSA with visual-step change strategy.

关键词： damage identification artificial fish swarm algorithm modal strain energy visual distance step length

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SIMULATION ON VESSEL INTELLIGENT COLLISION AVOIDANCE BASED ON artificial fish swarm algorithm

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POLISH MARITIME RESEARCH 2016年第s1期23卷 138-143页

作者： Li, Weifeng Ma, Wenyao Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China

TAs the rapid development of the ship equipments and navigation technology, vessel intelligent collision avoidance theory was researched world widely. Meantime, more and more ship intelligent collision avoidance products are put into use. It not only makes the ship much safer, but also lighten the officers work intensity and improve the ship's economy. The paper based on the International Regulation for Preventing Collision at sea and ship domain theories, with the ship proceeding distance when collision avoidance as the objective function, through the artificial fish swarm algorithm to optimize the collision avoidance path, and finally simulates overtaking situation, crossing situation and head-on situation three classic meeting situation of ships on the sea by VC++ computer language. Calculation and simulation results are basically consistent with the actual situation which certifies that its validity.

关键词： simulation collision avoidance artificial fish swarm algorithm

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