Particle swarm optimization algorithm based on clustering degree of swarm

A particle swarm algorithm and population technology, applied in the field of optimization algorithms, can solve the problems of population falling into local extreme points, strong singleness of the population, unfavorable global optimal solutions, etc., so as to reduce the possibility of falling into local extreme points and avoid The effect of premature convergence and improving global search capabilities

Inactive Publication Date: 2015-11-25
STATE GRID CORP OF CHINA +2
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Problems solved by technology

Although this method can obtain a faster convergence speed, it is easy to cause the population to fall into a local extreme point, which is not conducive to finding the global optimal solution. Especially...

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  • Particle swarm optimization algorithm based on clustering degree of swarm
  • Particle swarm optimization algorithm based on clustering degree of swarm
  • Particle swarm optimization algorithm based on clustering degree of swarm

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Embodiment Construction

[0027] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0028] In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, a feature defined as "first" and "second" may explicitly or implicitly include one or more of these features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0029] In the present invention, unless otherwise clearly specified...

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Abstract

The present invention discloses a particle swarm optimization algorithm based on a clustering degree of a swarm. The algorithm comprises the following steps of carrying out initialization; updating the swarm; judging whether a number of iterations is greater than a preset number of iterations and executing a corresponding step; judging whether a number of update iterations is greater than a preset number of times of stagnation and executing a corresponding step; calculating a particle clustering degree of each particle and a particle clustering degree of a swarm optimal position so as to acquire a distance between each particle and the swarm optimal position; according to a fitness of each particle, selecting a plurality of particles of which the number accords with a swarm scale to form a current swarm; and carrying out iterative optimization and updating until the maximum number of iterations is reached. According to the particle swarm optimization algorithm disclosed by the embodiment of the present invention, different evolutionary strategies can be adopted for different particles according to the progress of the optimizing process and the particle clustering degree so as to reduce the possibility of falling into the local minimum, improve the global searching ability of the algorithm and effectively avoid premature convergence.

Description

technical field [0001] The invention relates to the technical field of optimization algorithms, in particular to a particle swarm algorithm based on the aggregation degree of populations. Background technique [0002] The current particle swarm optimization (PSO) algorithm is a typical heuristic bionic algorithm, which originated from the simulation of the foraging behavior of birds, and its essence is to achieve optimization based on the information interaction between individuals and groups. It is a typical group optimization algorithm. [0003] Particle swarm optimization algorithm uses particles to represent a solution to the optimization problem, and its corresponding objective function value is called the fitness of particles, and multiple particles form a population. Each particle has a position and speed. In each iteration, each particle adjusts its own position and flight speed according to the best position found by the individual and the best position found by th...

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Application Information

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IPC IPC(8): G06F17/50
Inventor 韩忠晖陈浩胡斌奇胡伟周一凡田亦林
Owner STATE GRID CORP OF CHINA
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