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

An improved genetic algorithm and multi-objective optimization technology, applied in the field of optimization design, to achieve the effect of multiple selection opportunities, good engineering applicability, and expanded search range

Active Publication Date: 2018-03-27
NANJING UNIV OF SCI & TECH
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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 an

Method used

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Example Embodiment

[0071] Example 1

[0072] Aiming at the inner ballistic process of artillery firing, the optimization algorithm of the present invention is used to optimize the parameters of the propelling charge and the structural parameters of the inner bore, so as to obtain a better inner ballistic design scheme. The optimized design variables include: propellant mass ω i , gunpowder thickness e i , gunpowder aperture d 0i , the length of gunpowder l ci , the chamber volume V of the inner chamber structure 0 , constitute the design variable vector X, where the subscript i=1, 2, i=1 represents thin powder, i=2 represents thick powder. The objective function is the muzzle pressure P at the end of the inner ballistic trajectory g , the charge utilization coefficient η ω , the working volume utilization coefficient η g . The constraint function is the projectile velocity V g , the maximum pressure P m , the relative position η of the end of gunpowder combustion k .

[0073] According

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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

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Claims

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Application Information

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Owner NANJING UNIV OF SCI & TECH
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