Disturbance-based elite reverse learning particle swarm optimization implementation method

A technology of particle swarm optimization and reverse learning, applied to biological models, instruments, computing models, etc., can solve problems such as poor optimization accuracy, easy to fall into local extremum, slow convergence speed, etc.

Inactive Publication Date: 2016-03-23
WUHAN UNIV OF SCI & TECH
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Problems solved by technology

However, the PSO algorithm has the problems of easy to fall into lo

Method used

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  • Disturbance-based elite reverse learning particle swarm optimization implementation method
  • Disturbance-based elite reverse learning particle swarm optimization implementation method
  • Disturbance-based elite reverse learning particle swarm optimization implementation method

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Embodiment Construction

[0041] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, without limiting its protection scope.

[0042]The invention provides a realization method of elite reverse learning particle swarm optimization based on disturbance. In one embodiment, the steps for implementing the method include:

[0043] The first step is to initialize particle parameters

[0044] According to the process of the present invention, a probability P is set during parameter initialization, and the particle is controlled by the probability to update the position of the particle in the way of elite reverse learning or disturbance, and P is set to 0.3 during the particle execution process. At the same time, the specific properties of the particles will be set, the initial population size is N particles, and the position X and velocity V of each particle are also initialized. The initial setting of the number of particle iterations ...

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Abstract

The invention relates to a disturbance-based elite reverse learning particle swarm optimization implementation method. The technical scheme of the method comprises a first step of initializing a particle parameter, a second step of calculating particle fitness values and obtaining individual extrema and a global extremum, a third step of progressively decreasing inertia weight in a nonlinear manner, wherein a nonlinear progressive decrease manner not a linear progressive decrease manner is used to change the inertia weight so that a convergence speed and convergence precision of an algorithm are improved, a fourth step of determining a particle position updating mode, a fifth step of updating the individual extrema and the global extremum, and a sixth step of determining a particle continuing execution condition. The method is suitable for solving a function optimization problem, and the method has the high convergence speed and the high convergence precision and can effectively prevent from falling into a local optimum.

Description

technical field [0001] The invention belongs to the technical field of intelligent computing, in particular to a realization method of particle swarm optimization based on disturbance-based elite reverse learning. Background technique [0002] Particle Swarm Optimization (PSO) is a global optimization evolutionary algorithm proposed by scholars Kennedy and Eberhart in 1995 inspired by the foraging behavior of birds. solution to the target problem. [0003] Particle swarm optimization algorithm is a population-based optimization intelligent algorithm. All particles have an fitness value determined by the optimized function, which is used to evaluate the quality of the current position of the particle. The particle finds the optimal solution through its own iteration, and in each iteration, the particle updates its position by chasing the individual extremum pbest and the global extremum gbest. Assuming that a group consists of N particles, in the D-dimensional search space,...

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Application Information

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IPC IPC(8): G06N3/00
Inventor 李俊汪冲陈姚节李波胡威方国康
Owner WUHAN UNIV OF SCI & TECH
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