Global optimization system and method based on continuous motion learning automata

Abstract

A global optimization system and method based on continuous action learning automata are provided, wherein the system includes: an initialization module, a behavior selection module, an environment feedback module, an updating module and an output module, wherein: the initialization module initializes parameters of CALA algorithm and inputs results into the behavior selection module to conduct behavior selection, wherein the behavior is fed back through the application of the path environment and enters environment feedback module, thereby obtaining the corresponding environment feedback and the local optimal solution. The updating module updates the algorithm parameters according to the environment feedback, inputs the updated parameters into the behavior selection module to complete an iteration, and improves the smoothing function. The improved smoothing function is introduced into next iteration environment feedback module for performing iteration many times, thereby obtaining theextreme value point finally. The current environment feedback is input into the output module to output the optimal path as the global minimum. The invention is reasonable in design, introduces a smoothing function and adds a slope component for improvement, so that CALA can easily jump out of a local minimum solution, and the subsequent searching has directionality, thereby greatly improving theconvergence speed and the correct rate of the algorithm.

Description

technical field [0001] The invention relates to a technology in the field of learning automata optimization, in particular to a global optimization system and method based on continuous action learning automata. Background technique [0002] The stochastic function optimization method generally uses the probability mechanism to describe the iterative process of its solution, which is different from the deterministic point sequence in the deterministic functional method, thus showing that the important basis of the algorithm is randomness. Such algorithms have a wide range of applications and can often be used to solve large-scale continuous and discrete function optimization, combinatorial optimization and other problems. The algorithm can theoretically guarantee to converge to the global optimal value with probability 1, but it takes a lot of time. Because the learning automaton has the advantages of strong anti-interference ability and global optimization ability, it has ...

AI Technical Summary

Problems solved by technology

Although the classic continuous action set learning automaton algorithm can also converge to the best behavior, there are serious problems that it is easy to fall into local minima and the anti-noise ability is relatively weak.

Images