Variable neighborhood search method and system on cloud computing platform

Abstract

The invention discloses a variable neighborhood search method and system on a cloud computing platform. The which are variable neighborhood search method and system on the cloud computing platform used for overcoming the defects, of excessively-quick convergence and being weakened in the capacity of fleeing a local extreme value, existing in conducted variable neighborhood search based on a single living example. The method includes the steps of presetting a data collection and a plurality of initial solutions and storing the initial solutions to the data collection;, using a plurality of living examples and being based on at least one of the initial solutions to conduct neighborhood search on a solving space; for any living example, when a locally optimal solution which is obtained in a searching mode is superior to a worst solution in the data collection, using the locally optimal solution to update the worst solution, wherein the worst solution is the solution of a minimum value among initial solutions and/or locally optimal solution in the data collection. The living examples are adopted to simultaneously conduct search and results can be searched mutually among the living examples, therefore, solving space can be effectively expanded, the possibility of fleeing locally optimal possibility is improved, and furthermore a better overall optimal solution can be obtained.

Description

technical field [0001] The invention relates to a variable neighborhood search technology, in particular to a variable neighborhood search method and system on a cloud computing platform. Background technique [0002] Variable Neighborhood Search algorithm (VariableNeighborhoodSearch, referred to as VNS), is a trajectory-based meta-heuristic algorithm, which includes a dynamically changing neighborhood structure. It is used to solve combinatorial optimization problems and global optimization problems. [0003] The solution methods of combinatorial optimization problems can be divided into two categories: one is the exact algorithm, which performs a complete search of the solution space and can guarantee to find the optimal solution for small-scale problems; the other is the heuristic algorithm, which The integrity of the search of the solution space is given up, so the global optimality of the final solution cannot be guaranteed. [0004] Since there are a large number of ...

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Problems solved by technology

[0007] However, the current variable neighborhood search is based on a single instance, which has limited search ability for the solution space, and has disadvantages such as too fast convergence and weak ability to escape from local extremum.

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