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Super-multi-objective optimization method, system and terminal based on dynamic decomposition and selection

A multi-objective optimization and dynamic decomposition technology, applied in the computer field, can solve the problems of aggregation, solution set falling into local optimum, poor solution set diversity, etc.

Pending Publication Date: 2021-04-30
CHINA UNIV OF GEOSCIENCES (WUHAN)
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AI Technical Summary

Problems solved by technology

Although the DDEA algorithm can dynamically adjust the position of the reference point, when selecting the optimal individual, it is easy to make the optimal individual of the PF problem with a convex type gather in the middle of the PF, resulting in poor diversity of the solution set, and the solution set falls into a local optimum

Method used

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  • Super-multi-objective optimization method, system and terminal based on dynamic decomposition and selection
  • Super-multi-objective optimization method, system and terminal based on dynamic decomposition and selection
  • Super-multi-objective optimization method, system and terminal based on dynamic decomposition and selection

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Embodiment 1

[0110] (1) Contents of the invention (algorithm idea):

[0111] MOEA / DDS has also made modifications to the DDR strategy in DDEA, and proposed a new dynamic decomposition and selection strategy (DDS), which can better select a set of optimal diversity and convergence balance. Excellent solution set. The main highlights of the MOEA / DDS algorithm are:

[0112] (1) The vertical distance from the individual to the unit hyperplane is used as the evaluation criterion for the convergence of the solution set. The smaller the distance is, the better the convergence is, and vice versa. This evaluation criterion can be applied to any shape of PF. The advantage is that this evaluation standard is more fair and applicable when the real PF is unknown.

[0113] (2) When selecting the optimal individual, the relationship between convergence and diversity is adaptively adjusted according to the evolution stage, so that the selected solution set converges faster under the premise of ensuring...

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Abstract

The invention belongs to the technical field of computers, and discloses a super-multi-objective optimization method, system and terminal based on dynamic decomposition and selection, and the method comprises the steps: randomly initializing a population P with N individuals; selecting N excellent individuals as the next generation evolution filial generation P by using the DDS, and calculating the distance between the selected individual and the hyperplane and the distance between the reference points corresponding to the individuals; initializing a filial generation population O as a null set; aiming at N individuals in the parent generation, starting circular processing; initializing R used for storing offspring individuals, and selecting a mating individual from the current parent by utilizing MatingSelection; using SBX and PM for generating a filial generation R for the two parent generations, and adding the filial generation R into the filial generation population O; and repeatedly carrying out population selection until the maximum evolution algebra is reached. According to the method, the convergence of each generation of individuals and the diversity among the individuals are used as information of mating variation of the individuals, so that progenies can be promoted to evolve towards a better direction.

Description

technical field [0001] The invention belongs to the field of computer technology, and in particular relates to a super multi-objective optimization method, system and terminal based on dynamic decomposition and selection. Background technique [0002] At present, single-objective, multi-objective and super-multi-objective optimization problems are common problems in daily life. For single-objective optimization problems, the evolutionary algorithm (Evolutionary Algorithm, EA) uses the random search mechanism of natural evolution selection and natural evolution to solve such problems well. However, in multi-objective and super-multi-objective optimization problems, there are conflicting or mutually promoting relationships among the objectives. Therefore, multi-objective and ultra-multi-objective optimization are different from single-objective optimization, and single-objective optimization has a unique optimal solution. However, multi-objective and super-multi-objective op...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/12
CPCG06N3/126G06Q10/04
Inventor 王茂才包芊戴光明彭雷宋志明陈晓宇
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
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