The Advanced Ant Colony Algorithm and its Application
ICMTMA), 2011 Third International Conference（2011）
摘要
Ant Colony Optimization (ACO) is a novel bionic evolutionary algorithm for solving complex combinatorial optimization problems. This research approach lies at initial stage at present, and a new adaptive ant algorithm is proposed for the traditional ant algorithm easily appears precocious and stagnation behavior phenomenon in this paper. And the traditional parameter of pheromone of ant colony algorithm is self-adaptive adjusted. Selecting a number of typical TSP problems to experiment, the results are indicated that the new adaptive ant colony algorithm has a better ability to search the global optimal solution and have better stability and astringency.
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关键词
better ability,optimisation,self-adjusting systems,evolutionary computation,self-adaptive system,bionic evolutionary algorithm,ant colony optimization,ant colony algorithm,new adaptive ant algorithm,acs,better stability,new adaptive ant colony,advanced ant colony,tsp problem,traditional parameter,pheromone,stagnation behavior phenomenon,complex combinatorial optimization problem,novel bionic evolutionary algorithm,advanced ant colony algorithm,self-adaptive,traditional ant algorithm,adaptive ant colony algorithm,algorithm design and analysis,probability,convergence,acceleration,optimization
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