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An adaptive swarm intelligence algorithm candidate solution modification method for group information sharing

A technology of swarm intelligence and group information, applied in the field of computational intelligence, can solve problems such as low search efficiency

Inactive Publication Date: 2019-03-01
SOUTHWEST UNIVERSITY FOR NATIONALITIES
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The object of the present invention provides a method for correcting candidate solutions of an adaptive swarm intelligence algorithm for group information sharing, which solves the problem of low search efficiency in the prior art

Method used

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  • An adaptive swarm intelligence algorithm candidate solution modification method for group information sharing
  • An adaptive swarm intelligence algorithm candidate solution modification method for group information sharing
  • An adaptive swarm intelligence algorithm candidate solution modification method for group information sharing

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Embodiment 1

[0052] This embodiment provides an adaptive group intelligence algorithm candidate solution correction method for group information sharing.

[0053] The invention is applied to the MQHOA algorithm, and the main process of MOHOA includes three main stages: energy level stability, energy level transition, and scale reduction. The invention is applied to the energy level stabilization stage.

[0054] Take function F9 in the CEC2013 test function set as an example to find its minimum value. The domain interval of this function is [-100,100], and the minimum value is -600.

[0055] The expression of function F9 is as follows:

[0056]

[0057] The MQHOA algorithm with this method is used to search for the optimal solution of F9 in 2 dimensions, and the data of the operation process is stored for analysis. Each evolution records the fitness value and marks the changes in the algorithm process. The parameter is set to K=10, σ min = 1E-06. The error when the algorithm converges is 2.16...

Embodiment 2

[0085] This embodiment provides an adaptive group intelligence algorithm candidate solution correction method for group information sharing, which specifically includes:

[0086] Suppose that x(i, j, t) represents the current position of the i-th individual of the candidate solution in the j-th dimension during the t-th iteration, x lb (i, j, t) represents the historical optimal position of the i-th individual of the candidate solution in the j-th dimension during the t-th iteration, x gb (j, t) represents the best position of all individuals in the entire population in the j-th dimension during the t-th iteration after group information sharing. σ lb (i, j, t) represents the distance between the current position of the i-th individual of the candidate solution in the j-th dimension and the historical optimal position of the individual in the tth iteration, σ gb (i, j, t) represents the distance between the current position of the i-th individual of the candidate solution in the j-...

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Abstract

The invention relates to the field of computational intelligence, in particular to an adaptive swarm intelligence algorithm candidate solution correction method for group information sharing. The present invention uses social information as a primary guide to modify candidate solutions while preserving individual experience to some extent. The method determines the value of attraction factor by calculating the position relation of multiple reference points obtained by Gaussian sampling, and then adaptively determines the influence of individual and group knowledge. Correct candidate solutionsto the maximum extent possible, while maintaining personal experience as self-judgment. Taking MQHOA algorithm as an example, Gaussian sampling is used to represent the distribution of candidate optimal solutions. Thus, the method uses Gaussian sampling to generate a new reference point for correction. The historical best position of the candidate solution and the best position of the entire population are used to determine the position of the sampling center, and the distance relationship between the reference points is used to determine the value of the attraction factor, thereby determiningthe adoption ratio of social information and personal experience.

Description

Technical field [0001] The invention relates to the field of computational intelligence, and in particular to a method for correcting candidate solutions of an adaptive group intelligence algorithm for group information sharing. Background technique [0002] In the field of artificial intelligence, it is often necessary to analyze and process a large amount of collected data to obtain an optimal value, and use the optimal value to guide the control device to make corresponding control actions. The accuracy and speed of the optimal value is directly related to the accuracy and speed of the control device to make control actions. [0003] The swarm intelligence optimization algorithm mainly simulates the swarm behavior of insects, animal swarms, bird swarms, and fish swarms. These swarms find food in a cooperative manner. Each member of the swarm learns its own experience and the experience of other members. Constantly change the direction of search. The outstanding feature of the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 穆磊王鹏辛罡
Owner SOUTHWEST UNIVERSITY FOR NATIONALITIES
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