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Fitness scaling method for improving overall search capability of genetic algorithm

A global search and genetic algorithm technology, applied in the field of fitness calibration, can solve problems such as increasing population hierarchy, immature convergence, and affecting algorithm optimization performance

Inactive Publication Date: 2011-05-11
CHANGAN UNIV
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  • Description
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  • Application Information

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Problems solved by technology

[0003] The problem with the genetic algorithm is that sometimes the population evolution will converge to a non-global optimal solution, that is, the immature convergence problem
Each of the above calibration methods has disadvantages: linear calibration may cause negative fitness, which requires the introduction of other operations such as σ truncation; power scaling is highly related to the problem and requires a lot of experience in the selection of parameter values ​​in the algorithm. Improper selection of parameter values ​​will affect the optimization performance of the algorithm; exponential scaling may further increase the hierarchical nature of the population

Method used

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  • Fitness scaling method for improving overall search capability of genetic algorithm

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Embodiment 1

[0038] Taking an experiment based on the matching pursuit algorithm as an example, the effect of the fitness calibration method proposed by the present invention on improving the global search ability of the genetic algorithm is illustrated.

[0039]Matching pursuit algorithm (Matching pursuit) is an adaptive signal decomposition method, which can decompose the signal into a linear span of time-frequency atoms that best match the signal structure. Different from Fourier analysis and wavelet analysis, the basis function of matching pursuit is not pre-selected, but adaptively selected from a redundant function dictionary based on the principle of the best matching signal. This feature makes the matching pursuit algorithm more "flexible". ”, and better describe the non-stationary time-varying characteristics of the signal. In the matching pursuit algorithm, the time-frequency atom is defined as:

[0040] g γ ( t ...

Embodiment 2

[0071] The No. 5 turbogenerator unit of a thermal power plant in Northwest China is composed of a low-pressure cylinder, a high-pressure cylinder, a generator and an exciter. After an overhaul of the equipment, it was started to run, and it was found that the vibration of the bearing bush of the high-pressure cylinder of No. 5 generator set increased significantly, and the vibration of the No. 2 bearing bush of the high-pressure cylinder adjacent to the low-pressure cylinder was particularly severe, which was far greater than the vibration limit. In order to find the cause of the fault, the vibration signal here is taken as the focus of diagnostic analysis. Figure 4 It is the waveform diagram of the vibration signal, where the sampling frequency is 2000Hz and the data length is 1024. Observation shows that the vibration signal of the No. 2 bearing bush shows a strong regularity, and there is a phenomenon of signal modulation.

[0072] In order to detect whether there is a sh...

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Abstract

The invention relates to a fitness scaling method for improving overall search capability of a genetic algorithm, wherein a logarithmic function serves as a scaling function, and the fitness of every individual of every generation during population evolution is re-adjusted through logarithm arithmetic, so as to reduce the influence of a super individual on the population evolution, further avoid the premature convergence in the genetic algorithm and improve the overall search capability of the genetic algorithm.

Description

technical field [0001] The invention belongs to the technical field of intelligent computing, and specifically relates to a method for calibrating the fitness of each individual in the evolution of a genetic algorithm population, thereby avoiding premature convergence of the genetic algorithm and improving the global search ability of the genetic algorithm. Background technique [0002] Genetic algorithm is an intelligent computing method and a computing model that simulates the biological evolution process in nature. In essence, the genetic algorithm is an efficient and parallel global optimization algorithm. It is simple, versatile, and robust. It can well solve complex problems that are difficult to solve by traditional optimization methods, and effectively avoid large-dimensional problems in combinatorial optimization problems. Therefore, genetic algorithms have been widely used in function optimization, engineering design, artificial intelligence, machine learning, imag...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06N3/12
Inventor 高强王婉秦肖梅房祥波刘本超
Owner CHANGAN UNIV
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