Particle cluster intelligent method based on chaotic optimization mechanism

Abstract

The invention discloses a particle cluster intelligent method based on a chaotic optimization mechanism. In the initialization phase, classical distribution and Logistic mapping are adopted; the particle swarm is initialized by Kent mapping; carrying out global optimization search in an N-dimensional solution space by utilizing a particle swarm algorithm with a weighted inertia weight factor. A function is updated by utilizing speed and position iteration. When the updating of the local optimal solution of the particles in the particle swarm falls into stagnation, global optimization search isperformed by utilizing the ergodicity of chaotic motion, the ergodicity interval of a chaotic variable is amplified to the definitional domain of an optimization variable in an appropriate carrier mode, particles are assisted to gradually escape from a local extreme value when the global optimal solution of the particle swarm is caught in stagnation, and the optimization search of the particle swarm is finished and the global optimal solution is output. According to the method, the defects that a premature phenomenon generally exists in a cluster intelligent algorithm and a globally optimal solution cannot be effectively obtained in complex high-dimensional function optimization are overcome.

Description

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AI Technical Summary

Benefits of technology

This patented innovative technique called Parallel Swarms Intelligence (PSI) allows us to solve complicated computational problem by guiding the movement of multiple particles through their own motions rather than just one's movements alone. It also includes techniques like Chaos Mechanics or Random Dynamics which work well at small scales but cannot handle large scale simulations accurately due to its limitations. PSI improves efficiency over traditional methods while maintaining accuracy.

Problems solved by technology

This patents describes various techniques used by scientists during hydronephics simulations to optimize their performance under different conditions like resource allocation, environmental impacts, and other aspects related issues. These challenges include finding solutions quickly enough while avoiding excessively large amounts of data being collected due to imperfections within the numerical equation equations involved. Additionally, there may exist scenarios where even partial ones get stuck together resulting in poor overall efficiency when trying to solve real world optimized problems involving multiple variables simultanously.

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