Multi-task multi-objective evolutionary algorithm based on reconstructed source domain task solution
A multi-objective evolution and multi-task technology, applied in computing, computing models, resource allocation, etc., to achieve the effect of improving convergence speed and accuracy
Pending Publication Date: 2022-05-13
YANSHAN UNIV
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However, in actual production, there is a certain correlation between many problems to be optimized
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[0061] The technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments, but those skilled in the art should understand that the embodiments described below are only used to illustrate the present invention, and should not be regarded as limiting the scope of the present invention. Based on the implementation of the present invention, all other implementations obtained by those of ordinary skill in the art without making creative efforts all belong to the protection scope of the present invention.
[0062] A multi-task multi-objective evolutionary algorithm based on reconstructing the source domain task solution, combined with Figure 1 to Figure 9 As shown, this method starts from the two unsolved problems of high-quality solution finding and high-quality solution transfer. On the premise that the problems to be solved have certain similarities, it explores the relationship between tasks and avoids negative...
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The invention provides a multi-task multi-objective evolutionary algorithm based on reconstruction of a source domain task solution, on the premise that to-be-solved problems have certain similarity, the relationship between tasks is explored, the source domain task solution is reconstructed by using an inverse mapping model based on correlation analysis and an objective space alignment strategy, so that the source domain task solution becomes a high-quality solution, and the problem of the prior art is solved. And the process of assisting the target domain task optimization by using the source domain task is realized. According to the method, the correlation of the tasks in the target space is transferred to the decision-making space by utilizing the inverse mapping model, so that great help is provided for optimization of the tasks in the target domain by utilizing the correlation of the decision-making space. The source domain task promotes the optimization of the target domain task, the target domain optimization drives the progress of the source domain task, and the source domain task and the target domain optimization complement each other, so that the convergence speed and precision of the algorithm in a multi-task environment are improved.
Description
technical field [0001] The present invention relates to the technical field of multi-objective optimization algorithms, in particular to a multi-task multi-objective evolutionary algorithm based on reconstructing source domain task solutions. Background technique [0002] Multi-objective evolutionary algorithm (MOEAs) is an algorithm that simulates biological evolution behavior, starting from a selected initial population, and continuously updating the population individuals in the iterative process by adhering to the survival of the fittest mechanism. According to Darwin's "natural selection, survival of the fittest" law, it screens out the most suitable solution to a certain problem through iteration, inheritance, and evolution. In research, evolutionary algorithms benefit from their excellent search ability, and MOEAs are widely used in practical problems. [0003] As it stands, MOEAs work well for single multi-objective optimization problems (MOPs). However, in actual ...
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IPC IPC(8): G06F9/48G06F9/50G06N3/00
CPCG06F9/4881G06F9/5038G06N3/006
Inventor 韦钦楠杨景明呼子宇
Owner YANSHAN UNIV