Self-adaptive particle swarm optimization method solving traveling salesman problem

A technology of particle swarm optimization and traveling salesman problem, which is applied in the field of adaptive particle swarm optimization based on reverse learning and chaotic local search, can solve the problem of prematurity of particles falling into local extremum, loss of initial particle diversity, and influence on algorithm convergence effect, etc. problems, to achieve the effect of enhancing local optimization capabilities, avoiding premature convergence, and improving optimization performance

Inactive Publication Date: 2018-11-16
HUBEI UNIV OF TECH
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Problems solved by technology

[0008] In the standard PSO algorithm, the generation of initial particles is generally to randomly generate particles of a certain size within a given range, which will lead to the loss of diversity of initial particles, and make the initial solution of particles poor, which will

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  • Self-adaptive particle swarm optimization method solving traveling salesman problem
  • Self-adaptive particle swarm optimization method solving traveling salesman problem
  • Self-adaptive particle swarm optimization method solving traveling salesman problem

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[0032] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0033] please see figure 1 , a kind of adaptive particle swarm optimization method for solving the traveling salesman problem provided by the present invention comprises the following steps:

[0034] Step 1: Use the reverse learning method to obtain the initial population and set parameters;

[0035] According to the definition of reverse learning, the initial population P with a size of N is randomly generated first, and each individual X in the population is X=(x 1 , x 2 ,...,x n ), according to the reverse learning method (3) to generate the reverse po...

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Abstract

The invention discloses a self-adaptive particle swarm optimization method solving a traveling salesman problem. On the basis of existing standard particle swarm optimization, backward learning is adopted for initializing particle population, and an inertia weight w and a learning factor c of particle swarm optimization are regulated adaptively along with increase of number of iteration, so that the optimization capability of particle swarm optimization is improved; and in the later period of particle swarm optimization, chaotic local search is introduced so as to prevent particle swarm optimization from falling into local optimum. The improved algorithm is tested through a standard function, an obtained result is obviously superior to that of the standard PSO, and improved particle swarmoptimization is applied to optimization solution of the traveling salesman problem, so as to obtain a better optimization result.

Description

technical field [0001] The invention belongs to the field of problem optimization, and relates to an adaptive particle swarm optimization method for specifically solving the traveling salesman problem, and relates to an adaptive particle swarm optimization method based on reverse learning and chaotic local search for solving the traveling salesman problem. Background technique [0002] In the process of the development of human society, optimization problems generally exist in the aspects of people's life, study and work. For example, in life, people always hope to find a fast and convenient route from one place to another; in production, they hope to have the lowest energy consumption, etc. [0003] Reynolds proposed the Boid model by simulating the flight behavior of birds in the mid-1980s, and simulated the running track of birds in the computer. Each individual in the model has the ability to perceive, and can perceive changes in itself and the surrounding environment ,...

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Application Information

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IPC IPC(8): G06N3/00G06Q50/14
CPCG06N3/006G06Q50/14
Inventor 方娜万畅
Owner HUBEI UNIV OF TECH
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