Historical experience and real-time adjustment combination-based particle swarm optimization algorithm

A technology of particle swarm optimization and particle swarm algorithm, applied in the field of intelligent algorithm

Inactive Publication Date: 2016-06-01
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0005] Aiming at the defect that the standard particle swarm optimization algorithm (PSO) is prone to local optima when optimizing high-d

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  • Historical experience and real-time adjustment combination-based particle swarm optimization algorithm
  • Historical experience and real-time adjustment combination-based particle swarm optimization algorithm
  • Historical experience and real-time adjustment combination-based particle swarm optimization algorithm

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[0006] The present invention includes the following steps:

[0007] 1. Basic particle swarm optimization algorithm.

[0008] The particle swarm optimization algorithm generates a set of random solutions when the feasible solution space is initialized, and searches for the optimal value through iteration. A vector is used to describe the position and velocity of the particle in the solution space. Suppose the velocity and position of the i-th particle in the D-dimensional search space are denoted as V i =(v i1 ,v i2 ,...,v iD ) And X i =(x i1 ,x i2 ,...,x iD ). Each particle has a fitness value determined by the fitness function. The optimal position found by the particle so far is called the individual optimal position, denoted as P i =(p i1 ,p i2 ,...,p iD ), and the optimal position found so far by the entire group is called the global optimal position, denoted as P g =(p g1 ,p g2 ,...,p gD ). In each iteration, the particle updates its position by learning from the two best po...

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Abstract

The invention relates to a historical experience and real-time adjustment combination-based particle swarm optimization algorithm. The objective of the invention is to eliminate the defect of local optimum under a condition that a standard particle swarm optimization algorithm (PSO) is utilized to optimize a high-dimensional complex function. According to the historical experience and real-time adjustment combination-based particle swarm optimization algorithm, previous bad experience is considered in a speed update process, so that particles can be prevented from repeatedly searching previously-found poorest positions; the individual optimal information of each particle is fully utilized, so that the search ability of the algorithm can be improved; and the inertia weight of each particle is adaptively changed by using the adaptive value of each particle, and therefore, global and local search capacity can be adjusted effectively in real time. When the algorithm of the invention is used to optimize four standard test functions, the algorithm can effectively speed up global convergence rate and improve global optimization accuracy compared with the other three algorithms.

Description

technical field [0001] The invention belongs to the field of intelligent algorithms, and proposes to consider previous bad experiences in the speed update process, make full use of the individual optimal information of each particle, and use the fitness value of each particle to adaptively adjust each particle in real time The inertia weight of the algorithm can effectively speed up the global convergence speed and improve the global optimization accuracy. Background technique [0002] Eberhart and Kennedy proposed a new intelligent optimization method based on random swarms—Particle Swarm Optimization (PSO) by studying the predation behavior of birds and fish. The algorithm guides the optimization search according to the swarm intelligence produced by the organic combination of the individuality and sociality of the particles in the swarm. The PSO algorithm has the advantages of simple modeling, easy description and implementation, fast convergence speed, strong global sea...

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Application Information

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IPC IPC(8): G06N3/00
Inventor 马瑞邓剑波
Owner CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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