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Multi-population simulated annealing hybrid genetic algorithm based on similarity expelling

A hybrid genetic algorithm and simulated annealing technology, applied in the field of multi-population simulated annealing hybrid genetic algorithm, can solve the problems of premature convergence and poor local search ability of the algorithm

Inactive Publication Date: 2015-05-13
GUANGXI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the multi-population genetic algorithm still uses traditional genetic operations when implementing evolution, so the problems of premature convergence and poor local search ability of the algorithm have not been completely solved. room for improvement

Method used

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  • Multi-population simulated annealing hybrid genetic algorithm based on similarity expelling
  • Multi-population simulated annealing hybrid genetic algorithm based on similarity expelling
  • Multi-population simulated annealing hybrid genetic algorithm based on similarity expelling

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Experimental program
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Effect test

Embodiment 1

[0110] In order to verify the performance of the algorithm of the present invention, five data sets of ulysses16, oliver30, chn31, att48 and eil51 provided in the standard database TSPLIB were selected in the present embodiment to test the algorithm of the present invention. The test was performed on CPU Intel Core 2.20GHZ, Windows 7.0 operating system, under the environment of matlab2011a, the output precision is 10 -9 .

[0111] A multi-population simulated annealing hybrid genetic algorithm based on similarity exclusion, comprising the following steps:

[0112] Step 1: Coding;

[0113] In the genetic algorithm, the parameters of the problem are coded in a certain way, and the relationship between the coded bit string and the representation of the problem to be sought is corresponding to each other. Moreover, encoding and decoding need to be repeated in the process of solving. For solving TSP, there are many different coding methods, such as: path representation, nearest ...

Embodiment 2

[0182] In order to check and evaluate the beneficial effect of the algorithm of the present invention, the embodiment adopts 6 classic test functions to carry out comparative calculation, and the description of 6 classic test functions is as follows:

[0183] f 1 : DeJong f4 function, its analytical formula is shown in formula (2-11):

[0184] f 1 (x,y)=-(x 2 +2y 2 -0.4cos(3*PI*x)-0.6cos(4*PI*y)) (2-:11)

[0185] -10≤x,y≤10

[0186] f 1 The function is a multimodal function with four local maxima, of which only the global maximum is achieved at f(0,0)=1.

[0187] f 2 : Mexican Hat’s function, its analytical formula is shown in formula (2-12):

[0188] f 2 ( x , y ) = 0.5 - ...

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Abstract

The invention relates to a multi-population simulated annealing hybrid genetic algorithm based on similarity expelling. The multi-population simulated annealing hybrid genetic algorithm includes the following steps: coding is carried out; initialization parameters are set; initial populations are created; fitness values are calculated; selecting operation is carried out; interlace operation is carried out; mutation operation is carried out; gene overturning operation is carried out; simulated annealing Metropolis rules are judged; migration operation based on similarity expelling is carried out; optimal storage is carried out; judgment is ended. The migration operation based on similarity expelling particularly includes the following steps: calculating the fitness values of individuals in a source population and a target population; selecting the individual with the largest fitness value from the source population to serve as the individual to be immigrated; conducting similarity calculation; conducting expelling replacement. The multi-population genetic algorithm with simulated annealing operation can improve the local search capability of the multi-population genetic algorithm, and the algorithm can search for approximate solutions even though optimal solutions to a larger extent. The individual similarity judgment is additionally carried out, attention is paid to differences between the individuals, the diversity of populations is maintained, premature convergence of the genetic algorithm is avoided, the solving quality of the algorithm is improved, and the algorithm is closer to the optimal solutions.

Description

technical field [0001] The invention relates to an intelligent optimization algorithm, in particular to a multi-population simulated annealing mixed genetic algorithm based on similarity exclusion. Background technique [0002] Optimization problem refers to a kind of problem that needs to give a certain solution, and then give a standard to determine the optimal solution among many solutions. This kind of problem often exists in scientific research and practice. These problems may be multi-objective, discontinuous, and have certain constraints and nonlinearity, and are even difficult to analyze. Therefore, it is difficult to solve these problems with traditional numerical methods. Therefore, people are trying to find more effective methods. And an optimization algorithm is proposed. Some relatively mature optimization algorithms have been able to solve many simple problems, but what makes scholars feel powerless is that when dealing with some more complex systems, these or...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 薛海萍归伟夏
Owner GUANGXI UNIV
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