Decomposition-based high-dimensional multi-objective evolution method

Abstract

The invention provides a high-dimensional multi-objective evolution method based on decomposition. The method includes: generating reference vectors, decomposing a high-dimensional multi-objective optimization problem into a plurality of single-objective optimization sub-problems; constructing a sub-population of a single-target optimization sub-problem based on the reference vector; distributingindividuals to the sub-populations by using a distribution mechanism; the method comprises the following steps: constructing a neighborhood sub-population, selecting individuals for genetic evolutionby using the constructed neighborhood sub-population, selecting individuals with excellent performance in the population by using a designed local and global selection strategy to enter the next generation of population, and repeatedly executing an evolution process until the evolution process is ended and a Pareto solution set of a high-dimensional multi-objective optimization problem is obtained. According to the method, the problem solving complexity is effectively reduced, the problem that good balance between population convergence and diversity is difficult to guarantee through a multi-objective optimization algorithm is solved, a Pareto solution set with good diversity and convergence is obtained, the algorithm efficiency is effectively improved, and the global convergence and population diversity of the algorithm can be effectively guaranteed.

Description

technical field [0001] The invention relates to the field of intelligent optimization algorithms, in particular to a multi-objective evolution method. Background technique [0002] Many problems in real life are actually multi-objective optimization problems (MOPs), that is, optimization problems with two or more objectives. Since the multi-objective optimization problem has multiple conflicting objectives, the final solution is not an optimal solution but a set of Pareto solutions. As the number of objectives increases, the optimization problem increases from the original 2-3 objectives to 4 or more, and the problem becomes a high-dimensional multi-objective optimization problem (MaOPs). As the number of targets increases, the number of non-dominated individuals in the population increases sharply. When the number of targets increases to a certain number, almost all individuals in the population are non-dominated, so that the selection pressure of the evolutionary populati...

AI Technical Summary

Problems solved by technology

[0007] In order to overcome the deficiencies of the existing technology, aiming at the problem that the existing multi-objective evolutionary algorithm is difficult to effectively balance the convergence and diversity when solving high-dimensional objective optimization problems, a high-dimensional multi-objective evolutionary algorithm based on decomposition is proposed, mainly including reference Vector generation, subpopulation construction, population initialization, individual fitness evaluation, ideal point and extreme point calculation and update, target vector normalization, target vector and reference vector association, population non-dominated sorting, neighborhood subpopulation construction, crossover Variation individual selection, offspring population generation and environmental selection strategies

Images