Metaheuristic optimization algorithm based on GPU parallel computing and electronic equipment

Abstract

The invention relates to a meta-heuristic optimization algorithm based on GPU parallel computing and electronic equipment, and belongs to the technical field of optimization algorithms, and the optimization algorithm comprises the following steps: distributing independent video card video memory spaces for ants and baits in the algorithm, and transmitting initialization parameters into a GPU; comparing and evaluating the bait position based on iteration in the GPU, when the number of iterations reaches the maximum number of iterations, copying the position parameter of the bait with the optimal position from the video memory of the video card to the memory, releasing the video memory space of the video card, and outputting a result; wherein each iteration for comparing and evaluating the positions of the baits comprises the following steps of: calculating objective function values of the ants and the baits about position parameters in parallel, updating the positions of the baits according to the comparison of the objective function values, and comparing and determining the bait with the optimal position; and the position of each ant after selecting the target bait is calculated in parallel, and the positions of the ants are updated. According to the technical scheme, the real-time requirement in the application can be met.

Description

technical field [0001] The present application belongs to the technical field of optimization algorithms, and in particular relates to a meta-heuristic optimization algorithm based on GPU parallel computing and an electronic device. Background technique [0002] The general formulation of the optimization problem in engineering is to select a set of parameters (variables), so that the design index (objective) can reach the optimal value under a series of related constraints (constraints). There are many optimization algorithms at present, such as gradient descent method, Newton method, conjugate gradient method, meta-heuristic optimization method, etc. [0003] The gradient descent method is simple to implement. When the objective function is a convex function, the solution of the gradient descent method is a global solution. But in general, the solution is not guaranteed to be the global optimal solution, and the gradient descent method is not the fastest. Newton's method...

AI Technical Summary

Problems solved by technology

The disadvantage of the general meta-heuristic algorithm is that the calculation and solution speed is slow during the application process, and when facing a large-scale search problem, the ability to jump out of the local optimum is poor.

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