Multi-modal optimization system based on random point positioning algorithm of learning automaton

Abstract

The invention relates to a multi-modal optimization system based on a random point positioning algorithm of a learning automaton, and the system comprises an initialization module, a parameter selection module, an environment feedback module, a multi-modal random point positioning optimization module, and an output module. The initialization moduleinitializes system parameters. The parameter selection module performs iterative selection of parameters on each parameter sub-interval in the parameter search space, the parameters are optimized to obtain feedback, the feedback is input into the environment feedback module to obtain environment feedback, and the environment feedback is input into the multi-modal random point positioning optimization module to obtain estimated values of all current optimal parameters; and when the number of iterations in the multi-modal random point positioning optimization module reaches a preset maximum number of iterations, the multi-modal random point positioning optimization module inputs all the obtained optimal parameters to the output module, and the output module outputs an optimal parameter set corresponding to all the optimal parameters. Compared with the prior art, the system has the advantages that all global optimal parameters are found at the same time, and the application range of the random point positioning method is widened.

Description

technical field [0001] The invention relates to the field of multimodal optimization, in particular to a multimodal optimization system based on a learning automata random point positioning algorithm. Background technique [0002] The purpose of the parameter optimization problem is to find the optimal parameter setting in a given search space so as to achieve the maximum or minimum value of some known criterion. Multimodal parameter optimization problem is an important research direction in parameter optimization problem, and its purpose is to find multiple global optimal parameters in one execution of the algorithm. Multimodal parameter optimization problems exist in a large number of practical applications, such as distributed channel selection, image segmentation, service selection, robot path planning and many other practical optimization problems. These problems generally have more than one global optimal parameter. The global optimal parameters are calculated. [00...

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Problems solved by technology

[0006] The purpose of the present invention is to provide a multi-modal optimization system based on learning automaton random point positioning algorithm in order to overcome the defects in the above-mentioned prior art, and use the statistical characteristics of historical feedback information to find multiple global optimal parameters at the same time. Make up for the current inability to solve multi-modal parameter optimization problems

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