Method for solving combination and optimization problems using ant colony optimization technology based on Map Reduce

Abstract

The invention discloses a method for solving combination and optimization problems using an ant colony optimization technology based on Map Reduce and belongs to the technical field of solving the combination and optimization problems. The method for solving the combination and optimization problems using the ant colony optimization technology based on the Map Reduce comprises the following steps: dividing solution spaces of appointed combination and optimization problems according to amount of set mapper; in a Map period, every mapper independently executes an improved ant colony algorithm in parallel in a subproblem solution space acquired through division in the first step and searches a locally optimal solution; in the Reduce period, the reducer accepts all locally optimal solutions searched in different solution spaces by the mapper, and globally optimal solution is acquired according to a solution space division condition adopted in the first step; the globally optimal solution acquired currently by the reducer is output and the steps come to an end. The method for solving the combination and optimization problems using the ant colony optimization technology based on the Map Reduce is good in flexibility and capable of improving efficiency of solving a large-scale combination and optimization problems.

Description

technical field [0001] The present invention relates to a method in the technical field of combinatorial optimization problem solving, and specifically relates to a method for solving combinatorial optimization problems using MapReduce-based ant colony optimization technology. Background technique [0002] Ant Colony Optimization (ACO) is a bionic meta-heuristic algorithm, which is derived from the natural process of ant colony looking for food. It has the advantages of distributability and robustness, and is a better meta-heuristic algorithm. It is widely used to solve various combinatorial optimization problems, such as the optimal solution to the traveling salesman (TSP) problem with NP difficulty, the Job Shop scheduling problem, the quadratic assignment problem, and the multidimensional knapsack problem. In practical engineering applications, the ant colony algorithm is widely used in data analysis, robot collaboration problem solving, electric power, communication, wat...

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Problems solved by technology

[0003] After searching the literature of the prior art, it was found that the article Parallel Multicolony ACO Algorithm With Exchange of Solutions, Proceedings of the18th Belgium-Netherlands Conference on Artificial Intelligence, 2006: 409-410 (parallel multi-group ACO algorithm based on solution exchange) proposed the use of MPI ( Message Passing Interface) parallel programming technology implements the ant colony algorithm and solves the TSP problem in a multi-machine environment. The complex MPI programming model increases the difficulty of development

The article Scaling Populations of a Genetic Algorithm for Job Shop Scheduling Problems using MapReduce, Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science, 2010: 780-785 (Using MapReduce to expand the population of genetic algorithms for Job Shop scheduling problems) proposed the use of MapReduce technology to achieve Genetic algorithm is a method to solve the job shop scheduling problem of a certain scale. This method also has certain advantages, but this method requires multiple iterations of MapReduce, which limits the improvement of algorithm performance.

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