Memory elite population-based catastrophe adaptive large neighborhood search method and device

Abstract

The invention provides a catastrophe adaptive large neighborhood search method and device based on a memory elite population, and belongs to the technical field of artificial intelligence. The method comprises the following steps: constructing a task allocation optimization model; the model comprises a target function and a plurality of constraint conditions, the target function is used for representing the maximum completion time of the minimum robot, and the plurality of constraint conditions comprise a task allocation constraint condition and a task priority constraint condition; initializing a search population and an elite population; solving the model by iteratively executing the following steps until the number of iterations is greater than the number of terminating iterations: for each solution in the search population, performing neighborhood search on the current solution by adopting an adaptive large neighborhood search algorithm, and detecting whether the current solution is updated or not; if yes, correspondingly updating the elite population; and if not, randomly selecting a solution from the current elite population to update the current solution when the catastrophe condition is met, so that the non-memorability of the adaptive large neighborhood search process can be made up and the search depth can be improved.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a catastrophe adaptive large neighborhood search method and device based on a memory elite population. Background technique [0002] With the sharp increase in the number of online shopping, online payment, and mobile electronic merchants, the warehousing industry has developed rapidly. In today's warehousing environment, there are many types of goods, large order batches, and real-time task updates. Traditional warehousing and logistics systems have been unable to meet these requirements. , the application of smart warehouse is becoming more and more popular. [0003] At present, smart warehouses generally use multi-robot systems for rack handling and picking. Robots need to transport designated racks to corresponding picking stations according to order information, which involves task allocation. In this environment, the task refers to the transportation task o...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a catastrophe self-adaptive large-neighborhood search method and device based on the memory elite population, which is used to solve the defects of no memory and low search depth in the ALNS search process in the prior art, and to realize the compensation for the lack of memory in the ALNS search process. Memorability and the purpose of improving search depth

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