A Novel Global Artificial Fish Swarm Algorithm with Improved Chaotic Search

2012 ◽  
Vol 538-541 ◽  
pp. 2594-2597
Author(s):  
Ying Xu ◽  
Hon Gan Chen
Keyword(s):  
Differential Evolution ◽  
Step Length ◽  
Global Optimum ◽  
Local Optimum ◽  
Artificial Fish Swarm Algorithm ◽  
Chaotic Search ◽  
Chaos Search ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Evolution Period

The artificial fish swarm algorithm, it may be trapped in local optimum in the later evolution period and its search accuracy is dependent on step length which is hard to keep balance between rapidity and accuracy. Aimed at the defects of AFSA, a novel global artificial fish swarm algorithm is proposed in this paper, in which normal chaotic search on earlier stage is modified , and a differential evolution with improved chaos search was proposed to lead artificial fish into global optimum value. The experimental results show that the proposed algorithm is not only superior to traditional one but also can make the result greater.

Overview of Artificial Fish Swarm Algorithm and its Applications in Industrial Problems

2015 ◽  
Vol 815 ◽  
pp. 253-257 ◽  
Author(s):  
Nurezayana Zainal ◽  
Azlan Mohd Zain ◽  
Safian Sharif
Keyword(s):  
Optimization Algorithm ◽  
Social Behaviors ◽  
Global Optimum ◽  
Local Optimum ◽  
Intelligent Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Individual Fish ◽  
Intelligent Optimization Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Artificial fish swarm algorithm (AFSA) is a class of swarm intelligent optimization algorithm stimulated by the various social behaviors of fish in search of food. AFSA can search for global optimum through local optimum value search of each individual fish effectively based on simulating of fish-swarm behaviors such as searching, swarming, following and bulletin. This paper presents an overview of AFSA algorithm by describing the evolution of the algorithm along with all the improvements and its combinations with various algorithms and methods as well as its applications in solving industrial problems.

On the Computational Study of Artificial Fish Swarm Algorithm and its Improvement

2014 ◽  
Vol 687-691 ◽  
pp. 1480-1484
Author(s):  
Pei Zhen Peng ◽  
Yi Yu ◽  
Zhao Jia Wang ◽  
Min Jiang
Keyword(s):  
Computational Study ◽  
Global Optimum ◽  
Academic Community ◽  
Convergence Speed ◽  
Artificial Fish Swarm Algorithm ◽  
Take The Best ◽  
Optimal Value ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Stop Condition

Artificial Fish Swarm Algorithm (AFSA) since 2002 has been proposed by Dr. Li Xiao-lei more than ten years, and has been widely used in various engineering fields. However, since a lot of comprehensive standard running tests has not yet been made with the algorithm, it has not yet been unanimously recognized by the international academic community. By 34 Benchmark Functions tested with AFSA, the result evaluation for functions that are applicable and not applicable by AFSA is summarized. Also in order to overcome the drawbacks of Global Artificial Fish Swarm Algorithm (GAFSA) such as slow convergence and low precision, a modified GAFSA(MS_GAFSA) is proposed. Combined with GAFSA and Modified Simplex, the algorithm can improve the convergence speed and precision of optimization. When GAFSA converges to the global optimum nearby, a simplex is constructed and the algorithm switches to Modified Simplex method which will continue to optimize until a certain stop condition is satisfied. Take the best point of simplex vertex at this time as the optimal value. The computational results on 34 Benchmark functions show that MS_GAFSA does improve in optimizing accuracy and convergence speed.

Research on the Automatic and Optimized Pipe Routing Layout for Aero-Engines Based on Improved Artificial Fish Swarm Algorithm

2013 ◽  
Vol 437 ◽  
pp. 275-280 ◽  
Author(s):  
Yu Zhang ◽  
Xiao Lan Bai
Keyword(s):  
Rapid Development ◽  
Local Optimum ◽  
Artificial Fish Swarm Algorithm ◽  
Pipe Routing ◽  
Aero Engine ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Aero Engines ◽  
The Mathematical Model

With the rapid development of aerospace industries, how to realize the automatic and optimized pipe-routing layout for the aero-engine has become a hot issue to be urgently solved. For the issue, a novel automatic and optimized pipe-routing layout method based on the improved artificial fish swarm algorithm was put forward in this paper. First, the mathematical model of the pipe-routing layout problem was established. Then, in view of the deficiencies of the artificial fish swarm algorithm,chaos mutation was used to carry out easy and rapid searching as well as robust escape from the local optimum,and gradient setting was used to raise the convergence speed and the solving accuracy.Further, the improved artificial fish swarm algorithm was applied to the pipe-routing layout for the aero-engine. At the end, the effectiveness and feasibility of the proposed method was proved by a case study.

The Inversion Analysis for Mechanical Parameters of Dam Based on the Artificial Fish Swarm Algorithm

2013 ◽  
Vol 416-417 ◽  
pp. 1786-1790 ◽  
Author(s):  
Guang Yang ◽  
He Zhi Liu ◽  
Kai Zhu ◽  
Dong Sheng Liu
Keyword(s):  
Global Optimum ◽  
Mechanical Parameters ◽  
Element Analysis ◽  
Dam Foundation ◽  
Artificial Fish Swarm Algorithm ◽  
The Finite Element Analysis ◽  
Fault Fracture Zone ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Inversion Analysis

The artificial fish swarm algorithm adopts a new top-down way of think-ing,starting from the realization of individual behaviors of artificial fish and then re-aching the global optimum of the group through the local optimization of fish individuals of the group.Based on the finite element analysis softwareMSC.Marc, this article makes inversion analysis for mechanical parameters of dam.By combining the monitoring data of a dam,inversion analysis [ is made on the elastic modulus of the dam body,dam foundation,both dam shores and the fault fracture zone by using the artificial fish swarm algorithm.Results show that using artificial fish swarm algorithm to make inversion analysis on dam mechanical parameters not only has faster con-vergence rate but also have higher accuracy.

scholarly journals A lifetime optimization method of new energy storage module based on new artificial fish swarm algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 168781402110508
Author(s):  
Pengfei Zhi ◽  
Yongshuang Qi ◽  
Weiran Wang ◽  
Haiyang Qiu ◽  
Wanlu Zhu ◽  
...  
Keyword(s):  
Energy Storage ◽  
Optimization Method ◽  
Step Length ◽  
Artificial Fish Swarm Algorithm ◽  
Fish School ◽  
Battery Capacity ◽  
New Energy ◽  
Lifetime Optimization ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

The demand for new energy will continue to expand as the environment changes and fossil energy decreases. However, the instability of new energy has slowed down the development of new energy. The joint use of new energy and energy storage modules effectively solves the shortcomings of new energy. The article proposed a lifetime optimization method of new energy storage module based on new artificial fish swarm algorithm. Firstly the life model based on the battery capacity [Formula: see text], charging current [Formula: see text], and discharge current [Formula: see text] is built. Secondly, the deep learning method is used to improve the step length and speed change of artificial fish-school algorithm. Finally, the simulation platform detects the optimized parameters [Formula: see text]. The simulation results show that optimized parameters can help extend the life of the energy storage module.

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