Resource scheduling optimization method based on optimized niche genetic algorithm

Abstract

The invention discloses a resource scheduling method based on an optimized niche genetic algorithm. The method comprises the following steps: S1, building a resource scheduling optimization mathematicmodel based on the building of a multi-objective function and a multi-constraint condition; s2, performing weighting processing on the multi-objective function based on a weight particle swarm algorithm, 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 aclustering center; s4, selection, self-adaptive crossover, self-adaptive variation and niche elimination operation; and S5, judging whether a termination condition is met or not to obtain a final resource scheduling mode. The method aims at solving the problems that existing multiple targets are difficult to solve and prone to falling into a local optimal solution in resource scheduling. Accordingto the resource scheduling method based on the optimized niche genetic algorithm, the three processes of determining the weight of a multi-objective function, the radius of the niche and crossover and mutation operators are improved, the cost of a resource scheduling mode is effectively and remarkably reduced, and the processing time is shortened.

Description

technical field [0001] The invention relates to the field of resource scheduling, in particular to a multi-objective optimization method based on a niche genetic algorithm to solve resource scheduling problems. Background technique [0002] With the transition from traditional manufacturing to intelligent manufacturing, how to reduce costs and waste of resources has become a research hotspot. Applying intelligent optimization algorithm to study more effective, more scientific and more convenient resource scheduling method is an important and inevitable way to change the current resource scheduling optimization process based on resource scheduling experience alone. [0003] In recent years, the problem of resource scheduling has always been the top priority of improving production efficiency in industrial production, and more and more researchers have applied intelligent optimization methods to the field of resource scheduling. Most of the traditional production workshop sch...

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Problems solved by technology

Article [Yuan Shuipeng et al. Multi-objective steelmaking-improved continuous casting resource scheduling with fast non-dominated sorting genetic algorithm with elite strategy [J]. Computer Integrated Manufacturing System, 2019, 25(01): 119-128.] for steelmaking For the special process requirements of continuous casting scheduling, an optimal strategy based on adaptive grid method is proposed to improve the fast non-dominated sorting genetic algorithm with elite strategy, which effectively overcomes the easy loss of the optimal strategy when using the traditional Pareto dominance method to solve discrete problems Deficiency of Useful Information

With the gradual increase in the demand of the factory, new problems have emerged in the traditional intelligent optimization algorithm: not only the problem of completing production tasks, but also the problem of cost and resources should be considered at the same time. Therefore, how to find the optimal resource scheduling of multiple objective functions way and satisfy complex constraints become the focus of new problems

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