Multi-objective optimization improved genetic algorithm based on dynamic weight M-TOPSIS multi-attribute decision-making

Abstract

The invention discloses a multi-objective optimization improved genetic algorithm based on dynamic weight M-TOPSIS multi-attribute decision-making. The method includes the steps of first determining multi-objective optimization mathematical model and genetic algorithm parameters, and establishing a constrained feasible population and a population objective function matrix; then, calculating objective weights of objective functions by using an entropy weighting method, synthesizing the mixed dynamic weights of the objective functions, performing population individual sorting by using an M-TOPSIS method based on the dynamic weights, and obtaining a Pareto temporary solution set; assigning virtual fitness values to the individuals according to the sorting, and selecting an offspring population by using a proportional selection operator and a roulette method; next, performing crossing and mutation operations on the offspring population; finally, merging the Pareto temporary solution set and the offspring population after the mutation operation to generate a new population; obtaining an optimal solution and a Pareto optimal solution set until termination conditions of the algorithm is satisfied. The method of the invention can realize the multi-objective optimization and the multi-attribute decision-making process at the same time, provides a new solution for the multi-objective optimization problem, and has a high engineering practical value.

Description

technical field [0001] The invention relates to the field of optimization design, in particular to an improved genetic algorithm for multi-objective optimization based on dynamic weight M-TOPSIS multi-attribute decision-making. Background technique [0002] With the development of intelligent optimization algorithms, genetic algorithms have been widely used in engineering. The standard genetic algorithm (GA) can only deal with a single target or convert multiple targets into a single target through weight coefficients. Kalyanmoy D, AmritP and Sameer A et al. in the paper A fast and elitist multi-objective geneticgorithm in 2002: NSGA-II The improved non-dominated sorting genetic algorithm proposed in is the most widely used multi-objective optimization algorithm at present, and finally obtains the Pareto non-inferior solution set. However, in engineering applications, the final use plan is unique. The diversity of the Pareto solution set of the original multi-objective opti...

AI Technical Summary

Problems solved by technology

However, in engineering applications, the final use plan is unique. The diversity of the Pareto solution set of the original multi-objective optimization algorithm and the uniqueness of the engineering application plan lead to ambiguity. How to select the most needed design plan in the Pareto solution set It has become another focus of multi-objective optimization problems. In this field, a multi-attribute decision-making method based on analytic hierarchy process and fuzzy comprehensive evaluation has been formed. However, there is no inherent relationship between multi-objective optimization and multi-attribute decision-making. in series

Images