Particle swarm optimization algorithm based on Tent chaotic mapping and Pobal jump strategy

Through Tent chaotic mapping and gazelle jumping strategies, the particle swarm optimization algorithm is improved, and the problems of uneven initialization, local optimization and parameter sensitivity of particle swarm optimization algorithm in nonlinear system modeling and control are solved, achieving more efficient parameter identification and global search capabilities.

CN120373344AInactive Publication Date: 2025-07-25CHANGCHUN INST OF OPTICS FINE MECHANICS & PHYSICS CHINESE ACAD OF SCI
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Patent Information

Application Number
CN202510452443.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-11
Publication Date
2025-07-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing particle swarm optimization algorithm has problems in the modeling and control of nonlinear systems, which affects its application effect in the identification of parameters of complex nonlinear systems.

Method used

The Tent chaotic mapping is used to initialize particle positions, combine the gazelle jumping strategy and adaptive parameter adjustment mechanism, improve the particle swarm optimization algorithm, improve the search space coverage through Tent chaotic mapping, enhance population diversity, and use the gazelle jumping strategy to jump out of local optimality, dynamically adjust the learning factor and inertial weights to reduce parameter sensitivity.

Benefits of technology

It improves the global search capability and robustness of the algorithm, enhances the parameter identification accuracy and scope of application in complex nonlinear systems, avoids local optimal traps, and improves convergence speed and optimization effect.

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Abstract

The invention relates to a particle swarm optimization algorithm based on Tent chaotic mapping and a Panka jump strategy, which relates to the technical field of servo control, and comprises the following steps: performing population initialization; evaluating fitness and initializing an optimal solution; performing iterative updating and diversity detection; continuously updating individual optimum and global optimum; and implementing a pedal jump strategy. According to the method, Tent chaotic mapping is used for particle position initialization, the problem of uneven distribution of random initialization is solved, the coverage degree of a search space is improved, meanwhile, the population diversity is enhanced, the possibility that particles are gathered to a local extreme value is reduced, and the global search capability is improved; dynamically changing learning factors and inertia weights are adopted, so that the parameter sensitivity is reduced, the algorithm robustness is improved, different optimization problems are adapted, and the application range is widened; a Pedal jump strategy is adopted, local development is balanced while the global search ability is improved, local optimum is avoided while the convergence speed is improved, and it is guaranteed that a better solution instead of a local extreme value is found.
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