Smelting burdening method based on optimized ecological niche genetic algorithm

Abstract

The invention discloses a smelting burdening method based on an optimized ecological niche genetic algorithm, which comprises the following steps: S1, establishing a multi-objective function, proposing indexes of various parameters as multi-constraint conditions, and establishing a burdening optimization mathematical model; S2, performing weighting processing on the multi-objective function basedon a difference particle swarm optimization method, and converting the multi-objective model into a problem of a single-objective function; S3, dividing the population into K clusters according to a K-means clustering algorithm, and determining a clustering center; S4, performing selection, self-adaptive crossover, self-adaptive variation and niche elimination operation; and S5, judging whether atermination condition is met or not to obtain the final addition amount of the ingredients. According to the invention, problems of difficult multi-objective solving and easy falling into a local optimal solution and the like existing in batching optimization are solved; according to the smelting batching method based on the optimized ecological niche genetic algorithm, the three processes of determining the weight of the multi-objective function, the ecological niche radius and crossover and mutation operators are improved, the accuracy of the batching ratio is effectively and remarkably improved, and the cost is saved.

Description

technical field [0001] The invention relates to the technical field of batching, in particular to a smelting batching method based on an optimized niche genetic algorithm. Background technique [0002] How to reduce the cost of ingredients and improve product quality under the premise of ensuring the composition requirements and operating technology has recently become a research hotspot. Applying intelligent optimization technology to study more effective, scientific and convenient batching methods is an important and inevitable way to change the current batching optimization process that relies solely on batching experience, rarely considers batching costs, and lags behind in manual operations. [0003] In recent years, with the country's large investment in infrastructure and the vigorous advancement of China's industrialization, my country's total output and demand have been rising steadily, and more and more researchers have applied intelligent optimization methods to t...

AI Technical Summary

Problems solved by technology

As the demand of the factory gradually increases, new problems arise in the traditional intelligent optimization algorithm: not only the cost of ingredients, but also the quality of the product and the amount of raw materials must be considered. Therefore, how to find the best multi-objective function Batching ratios and satisfying complex constraints become new focus

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