Self-adaption artificial swarm optimization method based on historical information in running process

A technology of artificial bee colony optimization and historical information, applied in special data processing applications, instruments, electrical digital data processing, etc., to achieve the effect of improving efficiency, improving algorithm efficiency, and balancing global search costs

Inactive Publication Date: 2015-04-29
NORTHEASTERN UNIV
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AI Technical Summary

Problems solved by technology

[0007] In order to solve the problems existing in the prior art, the present invention provides an adaptive artificial bee colony optimization method based on historical information in the running process. Through multiple iterations, a solution that maximizes the global fitness value is finally found. On the basis ...

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  • Self-adaption artificial swarm optimization method based on historical information in running process
  • Self-adaption artificial swarm optimization method based on historical information in running process
  • Self-adaption artificial swarm optimization method based on historical information in running process

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Embodiment

[0063] Based on the above conclusions, the present invention proposes an algorithm for optimizing artificial bee colony parameters based on strategy adaptation. In the case of specifying the number of food sources and the number of iterations, the method of obtaining the global minimum is as follows: optimization problem, with n decision variables can be described as:

[0064] min y=F(x)=(x 1 , X 2 ,...X k ,..., x n ) (9)

[0065] subject to g j (x)≤0, j=1, 2,..., J

[0066] h i (x)=0, i=1, 2,..., K

[0067] (10)

[0068] Where g j (x k ) And h i (x k ) Are inequality constraints and equality constraints, x=(x 1 , X 2 ,..., x n ) Is an n-dimensional decision vector. In function optimization, there are a certain number of decision variables, these decision variables are continuous, each decision variable has an upper and lower bound, the decision variable x k The upper and lower bounds are set to: UB k , LB k Where x k Meet: LB k ≤x k ≤UB k . With the upper and lower bounds, the se...

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Abstract

The invention discloses a self-adaption artificial swarm optimization method based on historical information in the running process. The parallel research is conducted through various strategies, and the use ratio of the strategies is unceasingly regulated and guided according to the historical information in the running process. The Gaussian distribution which the important parameter Limit used for balancing the overall search cost in the method belongs to is unceasingly regulated according to the historical information in the running process. The use ratio of the search strategies is dynamically distributed through the self-adaption artificial swarm optimization method according to the historical information in the running process, and the important parameter for balancing the overall search cost is dynamically regulated. Under the condition that the numbers of the calculation times of fitness functions are the same, the method has a more superior and efficient function optimization effect.

Description

Technical field [0001] The invention belongs to the field of artificial intelligence, and is specifically an artificial bee colony optimization method based on historical information during operation. Background technique [0002] Whether it is design optimization in engineering practice, or planning and decision-making in social development and national economy, most of them can be attributed to optimization problems. Using optimization methods can better weigh the conflicting goals in these problems and obtain satisfactory optimization results, thereby improving the ability of mankind to transform nature and society. In real life, most optimization problems exceed NP difficulty, and it is difficult to find the best solution within the limited time and objective conditions. Therefore, how to quickly obtain an optimal or relatively satisfactory solution in a limited time and an objective environment has important theoretical and practical significance for improving productivity ...

Claims

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Application Information

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IPC IPC(8): G06N3/00G06F17/30
Inventor 张长胜刘婷婷张斌
Owner NORTHEASTERN UNIV
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