NSGA-II (non-domination sequencing genetic algorithm) multi-objective optimization searching method

A multi-objective optimization and search method technology, which is applied to the improvement field of the search method in the mainstream multi-objective optimization algorithm NSGA-II, can solve the problem of local convergence of the elite strategy, and prevent the search from converging in advance or falling into the local optimal solution, speeding up Search speed, the effect of increasing variety

Inactive Publication Date: 2012-12-19
JIANGSU UNIV
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AI Technical Summary

Problems solved by technology

[0006] The purpose of the invention is to solve the local convergence problem brought by the elite strategy in the NSGA-II search method

Method used

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  • NSGA-II (non-domination sequencing genetic algorithm) multi-objective optimization searching method
  • NSGA-II (non-domination sequencing genetic algorithm) multi-objective optimization searching method

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Embodiment 1

[0024] Such as figure 1 with figure 2 As shown, the NSGA-II multi-objective optimization search method includes the following steps:

[0025] (1) Randomly initialize the population as the first-generation parent population, with a size of N;

[0026] (2) Perform non-dominated sorting on the first-generation parent population, generate the first-generation sub-population after selection, crossover, and mutation, and then merge into a double population;

[0027] (3) Perform fast non-dominated sorting on the merged double population, first select the first-level non-dominated individual set N 1 , randomly drop D i Individuals are filled into the parent population of the next generation; among them, the number of individuals discarded at each level D 1 、D 2 ...D i ...D n It is proportional to the ratio of the number of non-dominated individual sets at this level to the population number, that is

[0028] D. 1 =N 1 *(N 1 / N),D 2 =N 2 *(N 2 / N),……D i =N i *(N i / N)...

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Abstract

The invention discloses an NSGA-II (non-domination sequencing genetic algorithm) multi-objective optimization searching method which comprises the following steps: randomly initializing a population as a first generation of father population, wherein the number of the population is N; carrying out non-domination sequencing on the first generation of father population, generating a first generation of child population after selection, intersection and heteromorphosis, and combining into a twice population; carrying out rapid non-domination sequencing on the combined twice population, firstly selecting a first stage non-domination individual set N1 and filling into a next generation of father population after abandoning D1 individuals randomly, and selecting a next stage non-domination individual set Ni (i=2, 3,..., n) in sequence and filling into a next generation of father population till the last stage of non-domination individual set Nn after abandoning Di individuals randomly, wherein the last stage is the set that the non-domination individual set is larger than a rest filling space of the next generation of father population; carrying out the congestion degree calculation on the last stage of non-domination individual set Nn after abandoning Dn individuals randomly; filling individuals with larger congestion distances into the rest space of the next generation of father population; carrying out genetic operation such as selection, intersection and heteromorphosis on the next generation of father population so as to generate the next generation of child population; and combining the next generation of child population and turning to the step (3).

Description

technical field [0001] The invention belongs to the research field of multi-objective optimization search method, in particular to the improvement of the search method in mainstream multi-objective optimization algorithm NSGA-II. Background technique [0002] Genetic Algorithm (Genetic Algorithm) is a kind of randomized search method evolved from the evolution law of the biological world (survival of the fittest, genetic mechanism of survival of the fittest). It was first proposed by Professor J.Holland in the United States in 1975. Its main feature is to directly operate on structural objects, without restrictions on derivation and function continuity; it has inherent implicit parallelism and better global optimization capabilities. ; Using a probabilistic optimization method, it can automatically obtain and guide the optimized search space, and adjust the search direction adaptively, without definite rules. These properties of genetic algorithms have been widely used in f...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/12
Inventor 周莲英杨鹤标蒋玲朱捷健
Owner JIANGSU UNIV
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