Mechanism isomorphism identification based on artificial fish swarm algorithm

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.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.97-101.3276 ◽

2010 ◽

Vol 97-101 ◽

pp. 3276-3280 ◽

Cited By ~ 4

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.

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

Journal of Sensors ◽

10.1155/2015/509143 ◽

2015 ◽

Vol 2015 ◽

pp. 1-17 ◽

Cited By ~ 3

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.

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An Improved Artificial Fish Swarm Algorithm and its Application

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.4434 ◽

2012 ◽

Vol 433-440 ◽

pp. 4434-4438 ◽

Cited By ~ 1

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.

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