The Novel Compound Evolutionary Optimization Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization

2010 ◽  
Vol 97-101 ◽  
pp. 3276-3280 ◽  
Author(s):  
You Xin Luo
Keyword(s):  
Optimization Algorithm ◽  
Optimization Design ◽  
High Efficiency ◽  
Evolutionary Optimization ◽  
Discrete Variables ◽  
Artificial Fish Swarm Algorithm ◽  
Complex Functions ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Mechanical Optimization

To overcome the problem of low convergence speed and sensitivity to local convergence with the traditional Artificial Fish-swarm Algorithm (AFSA) to handle complex functions, a novel compound evolutionary algorithm, called AFS-EMPCEOA, was introduced which is combined Artificial Fish-swarm Algorithm with the Elite Multi-parent Crossover Evolutionary Optimization Algorithm (EMPCEOA) that is GuoTao Algorithm improved by elite multi-parent crossover method. AFSEMPCEOA algorithm program with hybrid discrete variables was also developed. The computing example of mechanical optimization design shows that this algorithm has no special requirements on the characteristics of optimal designing problems, which has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence and high efficiency.

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.

Build a Game Dynamical System through the Optimization of the Artificial Fish Swarm Algorithm

2013 ◽  
Vol 325-326 ◽  
pp. 1712-1716
Author(s):  
Hou Min Wu ◽  
Shi Lei Xiao ◽  
Feng Qin Chen
Keyword(s):  
Dynamical System ◽  
Optimization Algorithm ◽  
Optimization Model ◽  
Game Engine ◽  
Artificial Fish Swarm Algorithm ◽  
Depth Study ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

The artificial fish swarm algorithm is a new bionic optimization algorithm, which focuses on constructing an optimization model of autonomous animats. Researches on it have been applied in many fields. This paper makes an in-depth study of the artificial fish swarm algorithm model, and tries to optimize and expand it for the use of building a game dynamical system, so as to serve as a technical and theoretical reference in designing and applying the game engine of AI movement.

Differential Evolution Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization

2012 ◽  
Vol 502 ◽  
pp. 402-406 ◽  
Author(s):  
Xiao Yi Che ◽  
Qi Yuan Liu ◽  
You Xin Luo
Keyword(s):  
Differential Evolution ◽  
Optimization Design ◽  
High Efficiency ◽  
Differential Evolution Algorithm ◽  
Discrete Variables ◽  
Search Mechanism ◽  
Engineering Mathematics ◽  
Complex Optimization ◽  
Evolution Algorithm ◽  
Mechanical Optimization

The optimization design about hybrid discrete variables is very significant but also difficult in engineering, mathematics for programming and operational research. Aimed at shortages of existing optimum methods, in this paper, according to the search mechanism of differential evolution algorithm (DEA), a new differential evolution algorithm is proposed to complex optimization problem with hybrid discrete variables The dynamic penalty function was constructed. DEA algorithm program with hybrid discrete variables is developed. The computing examples of mechanical optimization design show that this algorithm has no special requirements on the characteristics of optimal designing problems, it has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence and high efficiency.

Application of Modified Artificial Fish Swarm Algorithm in Power System Reactive Power Optimization

2013 ◽  
Vol 321-324 ◽  
pp. 1361-1364
Author(s):  
Shu Kui Liu ◽  
Na Dong ◽  
Zhi Zheng ◽  
Li Cheng ◽  
Qi Li
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Reactive Power Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Comparison Results ◽  
Artificial Fish Swarm ◽  
Network Losses ◽  
Swarm Algorithm

Modified Artificial Fish Swarm Algorithm (MAFSA) based on the global search characteristic of Artificial Fish Swarm Algorithm (AFSA), and combined with the local search of chao optimization algorithm(COA), can avoid trapping into local minimal value and decrease the iteration numbers, which was a swarm intelligence optimization algorithm applied to continuous space. MAFSA was proposed to optimize the reactive power optimization, which applied for optimal reactive power is evaluated on an IEEE 30-bus power system. The modeling of reactive power optimization is established taking the minimum network losses as the objective. The simulation results and the comparison results with various optimization algorithms demonstrated that the MAFSA converges to better solutions than other approaches and the algorithm can make effectively use in reactive power optimization. Simultaneously, the validity and superiority of MAFSA was proved.

scholarly journals Triaxial Accelerometer Error Coefficients Identification with a Novel Artificial Fish Swarm Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Yanbin Gao ◽  
Lianwu Guan ◽  
Tingjun Wang
Keyword(s):  
High Efficiency ◽  
Computational Cost ◽  
Calibration Method ◽  
Identification Algorithm ◽  
Triaxial Accelerometer ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Coefficients Identification ◽  
Error Coefficients

Artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligence techniques, which is widely utilized for optimization purposes. Triaxial accelerometer error coefficients are relatively unstable with the environmental disturbances and aging of the instrument. Therefore, identifying triaxial accelerometer error coefficients accurately and being with lower costs are of great importance to improve the overall performance of triaxial accelerometer-based strapdown inertial navigation system (SINS). In this study, a novel artificial fish swarm algorithm (NAFSA) that eliminated the demerits (lack of using artificial fishes’ previous experiences, lack of existing balance between exploration and exploitation, and high computational cost) of AFSA is introduced at first. In NAFSA, functional behaviors and overall procedure of AFSA have been improved with some parameters variations. Second, a hybrid accelerometer error coefficients identification algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for triaxial accelerometer error coefficients identification. Furthermore, the NAFSA-identified coefficients are testified with 24-position verification experiment and triaxial accelerometer-based SINS navigation experiment. The priorities of MCS-NAFSA are compared with that of conventional calibration method and optimal AFSA. Finally, both experiments results demonstrate high efficiency of MCS-NAFSA on triaxial accelerometer error coefficients identification.

Mechanism isomorphism identification based on artificial fish swarm algorithm

2021 ◽  
pp. 095440622097756
Author(s):  
Huijun Yi ◽  
Jianpei Wang ◽  
Yongle Hu ◽  
Ping Yang
Keyword(s):  
High Efficiency ◽  
Kinematic Chain ◽  
Topological Graph ◽  
Artificial Fish Swarm Algorithm ◽  
Cad System ◽  
Fish Model ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Mechanism Kinematic ◽  
Isomorphism Identification

The aim of this paper is to propose a practical solution for mechanism kinematic chain isomorphism identification – an artificial fish swarm algorithm. The artificial fish model of mechanism isomorphism identification is established, and behavioral way of the artificial fish is designed. According to isomorphism identification features of topological graph, the process of mechanism isomorphism identification based on artificial fish swarm algorithm is confirmed. The rationality and reliability of artificial fish swarm algorithm on the isomorphic identification of mechanism have been illustrated by a specific example, which provides a new method for intelligent CAD system design of mechanism. It builds a basis for future work in isomorphism identification of mechanism with high efficiency. Isomorphic identification of mechanism will contribute to rational qualitative analysis of mechanism design, perfection of irrationality can be done timely, which is the key factor for mechanical manufacturing. In this paper, we introduce the mechanism kinematic chain firstly, then optimization of artificial fish swarm algorithm is illustrated, and it is shown that how fish swarm algorithm is applied to mechanism kinematic chain. Finally, the feasibility and efficiency of the method are verified by the example of 10 bars, and the complex mechanism can be identified by the example of 14 bars and 18 bars.

An Improved Artificial Fish Swarm Algorithm and its Application

2012 ◽  
Vol 433-440 ◽  
pp. 4434-4438 ◽  
Author(s):  
Xin Guan ◽  
Yi Xin Yin
Keyword(s):  
High Efficiency ◽  
Competitive Exclusion ◽  
Local Extremum ◽  
Test Functions ◽  
Practical Application ◽  
Invasive Weed ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Benchmark Test Functions

An improved algorithm (AFSA-IWO) was developed based on the artificial fish swarm algorithm (AFSA) and invasive weed optimization (IWO). It introduces IWO, and improves its mechanism of the competitive exclusion to meet practical application. Convergence analysis was performed with some typical benchmark test functions and comparison was made with AFSA. At the same time, it uses the AFSA-IWO to optimize the PID parameters. The results showed that the approach presented better ability in leaping over the local extremum and enhancing local exploration, and can void blind searching in the later evolution period. So it is a global optimization algorithm with good feasibility and high efficiency.

Evolutionary Cellular Automata Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization

2012 ◽  
Vol 271-272 ◽  
pp. 912-916
Author(s):  
Wei Yue Xiao ◽  
Yue Hua Cai ◽  
You Xin Luo
Keyword(s):  
Optimization Design ◽  
High Efficiency ◽  
Continuous Variables ◽  
Discrete Variables ◽  
Reliable Operation ◽  
Engineering Mathematics ◽  
Complex Optimization ◽  
Mechanical Optimization ◽  
Optimal Designing ◽  
Cellular Automata Algorithm

The optimization design about hybrid discrete variables synthesizing integer, discrete and continuous variables is very significant but also difficult in engineering, mathematics for programming and operational research. Aimed at shortages of existing optimum methods, in this paper, Evolutionary Cellar Automata Algorithm (ECAA) is proposed to complex optimization problem with hybrid discrete variables which has a digging operator and two learning operators (dual arithmetic crossover operator and chaos-peak-jumping operator). The computing examples of mechanical optimization design show that this algorithm has no special requirements on the characteristics of optimal designing problems, it has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence and high efficiency.

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