Artificial intelligence global optimization method based on QUATRE architecture

Abstract

The invention discloses an artificial intelligence global optimization method based on a QUATRE architecture. The artificial intelligence global optimization method comprises the steps of: S1, establishing a quasi-affinity transformation evolution architecture which is shown in description; S2, setting an evaluation function, a global optimal value, an iteration threshold value, a maximum number of evaluation function calling times and a condition for carrying out iteration of the quasi-affinity transformation evolution architecture, and initializing the quasi-affinity transformation evolution architecture; S3, judging whether the condition for carrying out iteration is met, if so, continuing the execution of following steps, and if not, stopping the execution; S4, calculating a variation matrix B, and acquiring a next generation particle population X<G+1> of a particle population X<G> according to the quasi-affinity transformation evolution architecture; S5, updating the number of evaluation function calling times and the next generation particle population; S6, acquiring a Boolean evolution matrix M and a correlated evolution matrix M-bar, acquiring a current optical particle in the particle population in (G+1)th iteration according to an optimal value of the evaluation function, and returning to the step S3 for judgment after making G=G+1. The artificial intelligence global optimization method based on the QUATRE architecture has the advantages of few parameters and high efficiency.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, in particular to an artificial intelligence global optimization method based on a QUATRE framework. Background technique [0002] As a branch of artificial intelligence, computational intelligence originated in the middle of the 20th century. Its main idea is to study complex data, observe experimental progress, and solve complex problems that cannot be solved by traditional mathematical formulas and models in reality. . Fuzzy logic, artificial neural network, memetic computing, evolutionary computing, quantum computing, etc. all belong to this field. [0003] Among them, in the field of evolutionary computing, there are mainly the following branches: genetic methods, learning classification systems, evolutionary programming, evolutionary strategies, and swarm intelligence. Among them, various methods in the swarm intelligence branch under the field of evolutionary computing solve complex...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above technical problems, the purpose of the present invention is to propose a new evolutionary architecture under the branch of swarm intelligence in the field of artificial intelligence, which can not only achieve the purpose of global optimization by realizing the uniform search of the "solution space", thereby eliminating the difference The defects in the evolution method and the particle cluster method can also reduce the implementation complexity of this method in practical applications by setting fewer parameters

Images