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Improved sparrow search method based on chaos reverse learning and adaptive spiral search

A technology of reverse learning and search method, applied in the field of improving sparrow search, can solve the problems of high algorithm calculation efficiency, weakened population diversity, easy to fall into local optimum, etc., to expand the search range, speed up convergence, and enhance global search ability. Effect

Pending Publication Date: 2022-05-13
XIDIAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, similar to other swarm intelligence optimization algorithms, SSA also suffers from the weakening of population diversity in the late stage of iteration, the algorithm has high computational efficiency, and is prone to problems such as local optimum.

Method used

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  • Improved sparrow search method based on chaos reverse learning and adaptive spiral search
  • Improved sparrow search method based on chaos reverse learning and adaptive spiral search
  • Improved sparrow search method based on chaos reverse learning and adaptive spiral search

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0078] See figure 1 , figure 1 It is a schematic flow chart of COSSA provided by the embodiment of the present invention, which includes:

[0079] Step 1: Initialize the parameters of the SSA algorithm, and use the chaotic reverse learning strategy to initialize the population.

[0080] First, assuming that the initial size of the sparrow population is n, use X={X 1,1 ,X 1,2 ,...,X 2,1 ,...,X n,d} represents; among them, d represents the dimension number of the problem to be solved. Initialize the initial position of sparrow group members in the solution space, the ratio of discoverers and followers in the population, and the maximum number of iterations t max , warning value R, safety value ST, random value Q and other parameter values.

[0081] Then, the population is initialized using the chaotic reverse learning strategy.

[0082] A) combine the chaotic mapping function and the reverse learning strategy to construct the chaotic reverse learning mathematical model; ...

Embodiment 2

[0142] Below by comparing the COSSA algorithm of the present invention with the existing SSA algorithm, to verify the beneficial effects of the present invention.

[0143] 1. Test conditions:

[0144] On the same experimental platform, set the initial population number to 50 and the maximum number of iterations to 300. Both algorithms are programmed using MATLAB R2016b, the computer operating system is Windows 10, and the processor is [Intel Core i7-4710MQ] 16GB.

[0145] 2. Test content and result analysis:

[0146] In this experiment, the chaotic map in COSSA uses the Logistic map mapping algorithm. The test function adopts the F1-F23 test function in the first embodiment above.

[0147] 2.1. In view of the fact that F1-F13 used in this embodiment are multidimensional functions, these 13 functions are solved when Dim=30, 100, 500, and 1000. Due to the randomness of the algorithm solution, all the algorithms are run independently for 30 times, and the results are shown in T...

Embodiment 3

[0162] On the basis of the first embodiment above, this embodiment provides an improved sparrow search device based on chaotic reverse learning and adaptive spiral search. See image 3 , image 3 It is a structural schematic diagram of an improved sparrow search device based on chaotic reverse learning and adaptive spiral search provided by the embodiment of the present invention, which includes:

[0163] Initialization module 1 is used to initialize the SSA algorithm parameters, and utilizes the initial population of chaos reverse learning strategy;

[0164] Calculation module 2 is used to calculate the initial fitness value of each individual in the population, and determine the optimal individual position;

[0165] The first update module 3 is used to update the positions of the finder and the follower in the population respectively by adopting an adaptive spiral search strategy;

[0166] The second update module 4 is used to update the position of the warning individual...

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Abstract

The invention discloses an improved sparrow search method based on chaos reverse learning and self-adaptive spiral search, which comprises the following steps of: 1, initializing algorithm parameters, and initializing a population by using a chaos reverse learning strategy; 2, calculating an initial fitness value of each individual in the population, and determining an optimal individual position; 3, updating the positions of a discoverer and a follower in the population by adopting a self-adaptive spiral search strategy; 4, updating the positions of the early warning individuals in the population; 5, updating the position of each individual in the population by adopting a chaos reverse learning strategy; 6, updating the fitness value and the optimal individual position of each individual in the population, and retaining part of dominant individuals; 7, if it is judged that the current number of iterations reaches the maximum number of iterations, the optimal individual position is output; otherwise, returning to the step 3 to continue execution. According to the method, population diversity is enhanced, and the problem that an existing algorithm is prone to falling into local optimum is avoided.

Description

technical field [0001] The invention belongs to the technical field of swarm intelligence algorithms, in particular to an improved sparrow search method based on chaotic reverse learning and self-adaptive spiral search. Background technique [0002] The swarm intelligence optimization algorithm is to search for the optimal fitness in a certain solution space to obtain the optimal solution by simulating the behavior rules of some creatures in nature. Because the swarm intelligence optimization algorithm has the advantages of simple implementation, clear principle, and easy expansion, it is more and more widely used in various optimization fields. [0003] At present, the existing swarm intelligence optimization algorithms mainly include ant colony algorithm, particle swarm optimization algorithm, bacterial colony optimization algorithm, leapfrog algorithm, artificial bee colony algorithm and sparrow search algorithm. Among them, Sparrow Search Algorithm (SSA) is a swarm inte...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00G06N7/08
CPCG06N3/006G06N7/08
Inventor 戴奉周周璇
Owner XIDIAN UNIV
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