Method, device and equipment for solving multi-objective optimization problem based on teaching and learning algorithm

Abstract

The embodiment of the invention discloses method, device and equipment for solving a multi-objective optimization problem based on a teaching and learning algorithm. The method comprises the steps: obtaining data of the multi-objective optimization problem; for the data of the multi-objective optimization problem, determining knowledge values of students in the class; sorting the knowledge valuesof the students in the Pareto solution sets at different levels to obtain a first sorting result; determining first M students with knowledge values ranked from large to small in the first sorting result as M teachers in the class, and determining other students except the M teachers in the first sorting result as students; updating students in the class through the teaching stage; updating students in the class through the learning stage; and if the current iteration number meets the preset maximum iteration number, outputting a Pareto solution set for the multi-objective optimization problem. By implementing the method for solving a multi-objective optimization problem based on a teaching and learning algorithm, the search range can be enlarged, and local optimum is prevented from beingentered in advance, so that a better Pareto leading edge can be obtained.

Description

technical field [0001] The invention relates to the technical field of computer algorithms and management optimization, in particular to a method, device and equipment for solving multi-objective optimization problems based on teaching and learning algorithms. Background technique [0002] Multi-objective Optimization Problem (MOP) involves all aspects of life, and related solving methods play a vital role in solving planning and decision-making problems in various aspects such as politics, finance, military, environment, manufacturing, and social security. effect. Multi-objective optimization problems can also be called multi-objective programming problems. Usually, the MOP problem can be described as shown in formula (1): [0003] [0004] In the above formula (1), x is the solution vector in the solution space E; F is the objective function vector; n is the number of sub-objective functions; g i (x) is an equality constraint in general form; h j (x) is the general ...

AI Technical Summary

Problems solved by technology

[0006] In practical applications, it can be found that the search range of the above method is small, and it is easy to enter local convergence in advance, resulting in a poor Pareto front.

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