Sparse ant system supporting large-scale combination optimization problem and solving method

Abstract

The invention provides a sparse ant system supporting a large-scale combination optimization problem. The system comprises an initial solution construction module, an iterative solving module, a sparse pheromone matrix update module and an optimal solution calculation module. The invention furthermore provides a method for calculating an optimal solution of the large-scale combination optimizationproblem by using the sparse ant system. The method comprises the steps of 1, constructing an initial solution; 2, calculating optimal solutions by multi-time iteration, calculating global iterative optimal solutions in M ants, circulating each ant for N times, obtaining a sub-step solution node j during each calculation, finally obtaining feasible solutions of current iteration, selecting the global optimal solution in the feasible solutions of the M ants, and performing comparison and update on the global optimal solution of the current iteration and the iterative optimal solution so far; 3,after each iteration is finished, updating a sparse pheromone matrix according to the global iterative optimal solution; and 4, performing loop iteration according to rules of the steps 2 and 3, until the defined iterative frequency or total cost of the feasible solutions is smaller than or equal to a target optimal solution.

Description

technical field [0001] The invention relates to the technical field of applied mathematics and theoretical computer science, in particular to the application field of a sparse ant system supporting large-scale combinatorial optimization problems. Background technique [0002] Combinatorial optimization problem is one of the hot issues in the fields of applied mathematics and theoretical computer science, and has important theoretical and practical significance. The goal of combinatorial optimization problems is to find the optimal object in a limited object set. Its main feature is that the set of feasible solutions can be regarded as or approximately as discrete, and the solution nodes can be gradually constructed. [0003] In order to solve combinatorial optimization problems, ant colony algorithm came into being. It effectively solves the combinatorial optimization problem with NP characteristics by simulating the foraging behavior of ant colonies in nature. It is a heur...

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Problems solved by technology

[0005] In view of this, the inventive idea of ​​the present invention is to address the shortcomings of the ant system in the prior art, and propose an ant system based on sparse matrix storage and operation, so as to solve the problem that the geometric space complexity of the traditional ant system cannot support large-scale State of the art combinatorial optimization problems

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