Optimal solution to distribution decision problem of distribution centre by artificial fish swarm algorithm

Due to the large amount of calculation and high time-consuming in traditional grayscale matching, this paper combines artificial fish algorithm of swarm intelligence with edge detection and the operation of bitwise exclusive or, and presents a fast method on feature matching. The method regards the problem of image matching as a process of searching the optimal solution. In order to provide artificial fish swarm algorithm with an appropriate fitness function, the operation of bitwise exclusive or and addition is employed to deal with the edge information extracted from the template image and the searching image. Then the best matching position is gradually approaching by swarming, following and other behaviors of artificial fish. Experimental results show that the proposed method not only significantly shortens the matching time and guarantees the matching accuracy, but also is robust to noise disturbance.

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The Location Selection of Distribution Centre Based on DNA Artificial Fish Swarm Algorithm

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2016.5061 ◽

2016 ◽

Vol 13 (2) ◽

pp. 1406-1414 ◽

Cited By ~ 2

Author(s):

Teng Fei ◽

Liyi Zhang ◽

Yunshan Sun ◽

Lei Chen ◽

Hongwei Ren

Keyword(s):

Location Selection ◽

Artificial Fish Swarm Algorithm ◽

Distribution Centre ◽

Artificial Fish Swarm ◽

Swarm Algorithm ◽

Selection Of

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A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzyc-Means Clustering

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/120495 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Li Ma ◽

Yang Li ◽

Suohai Fan ◽

Runzhu Fan

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Clustering Algorithms ◽

Optimal Solution ◽

Medical Image Segmentation ◽

Superior Result ◽

Artificial Fish Swarm Algorithm ◽

Artificial Fish Swarm ◽

Fcm Clustering ◽

Swarm Algorithm

Image segmentation plays an important role in medical image processing. Fuzzyc-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).

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The Modified Artificial Fish Swarm Algorithm for Least-Cost Planning of a Regional Water Supply Network Problem

Sustainability ◽

10.3390/su11154121 ◽

2019 ◽

Vol 11 (15) ◽

pp. 4121 ◽

Cited By ~ 1

Author(s):

Yi Liu ◽

Zhengpeng Tao ◽

Jie Yang ◽

Feng Mao

Keyword(s):

Water Supply ◽

Optimal Solution ◽

Supply Network ◽

Water Supply Network ◽

Artificial Fish Swarm Algorithm ◽

Planning Optimization ◽

Cost Planning ◽

Artificial Fish Swarm ◽

Swarm Algorithm ◽

Regional Water

A regional water supply network (RWSN) plays a key role in the process of urbanization development. This paper researches the planning optimization model of a regional water supply network with the payoff characteristic between economical cost and reliability, in which the hydraulic-connectivity is selected as the surrogate measure of the reliability in the regional water supply network. The modified artificial fish swarm algorithm (MAFSA) is proposed to solve the optimization problem by adjusting research visual and the inertia weights of artificial fish swarm algorithm (AFSA) according to the hydraulic-connectivity. The experiment results of regional water supply network show that MAFSA can effectively obtain the optimal solution with the maximum reliability and least cost compared to other algorithms, which can thereby achieve the optimization of RWSN engineering applications.

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Application of Improved Artificial Fish Swarm Algorithm to Slope Stability Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.532-533.1861 ◽

2012 ◽

Vol 532-533 ◽

pp. 1861-1866

Author(s):

Jia Liu ◽

Jing Li ◽

Jun Yi Huo ◽

Li Na Liu

Keyword(s):

Stability Analysis ◽

Slope Stability ◽

Simulated Annealing Algorithm ◽

Extreme Points ◽

Optimal Solution ◽

Failure Surface ◽

Slope Stability Analysis ◽

Artificial Fish Swarm Algorithm ◽

Artificial Fish Swarm ◽

Swarm Algorithm

The Sweden method of slices is used as the data model for slope stability analysis and a new searching method for slope stability analysis is presented. This method, named SAA_IAFSA in this paper uses the artificial fish-swarm algorithm based on simulated annealing algorithm to search the critical failure surface and minimum safety factor. This method can overcome the disadvantages of traditional optimization method such as easy to fall into local extreme points with high accuracy, applicability, and can get a more accurate global optimal solution. Finally, the feasibility and effectiveness of the new approach is verified by practical problem. The experimental results show that the proposed algorithm is significantly superior to original AFSA, with wide application.

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Research of Multi-Objective Power Network Planning Based on Improved Fish Swarm Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.494-495.1735 ◽

2014 ◽

Vol 494-495 ◽

pp. 1735-1738

Author(s):

Xin Yi ◽

Li Xin Ma ◽

Wen Fei Cai ◽

Jin Sun

Keyword(s):

Network Planning ◽

Optimal Solution ◽

Artificial Fish Swarm Algorithm ◽

Multi Objective ◽

Planning Scheme ◽

Degree Of Satisfaction ◽

Artificial Fish Swarm ◽

Power Network Planning ◽

Swarm Algorithm ◽

Grid Planning

According to the problem that traditional single objective network planning cant meet the demand of complex gird and key supply mission, using improved artificial fish swarm algorithm (AFSA) for multi-objective grid planning. The paper presents a method through selecting Pareto optimal compromise solution by calculating and comparing the degree of satisfaction of each planning scheme with the reference of scholar Paretos approach of striking a multi-objective optimal solution. This method can effectively balance weights between multiple targets and improve the reliability and economy of power grid. The experiment results show the good performance of the proposed method.

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