CAJ | 학술논문

蚁群算法是一种应用广泛、性能优良的智能优化算法,其求解效果与参数选取息息相关.鉴于此,针对现有基于粒子群参数优化的改进蚁群算法耗时较大的问题,提出一种新的解决方案.该方案给出一种全局异步与精英策略相结合的信息素更新方式,且通过大量统计实验可以在较大程度上减少蚁群算法被粒子群算法调用一次所需的迭代代数.仿真实验表明,所提出算法在求解较大规模旅行商问题时具有明显的速度优势.
의군산법시일충응용엄범、성능우량적지능우화산법,기구해효과여삼수선취식식상관.감우차,침대현유기우입자군삼수우화적개진의군산법모시교대적문제,제출일충신적해결방안.해방안급출일충전국이보여정영책략상결합적신식소경신방식,차통과대량통계실험가이재교대정도상감소의군산법피입자군산법조용일차소수적질대대수.방진실험표명,소제출산법재구해교대규모여행상문제시구유명현적속도우세.
@@@@Ant colony optimization(ACO) algorithm is an intelligent algorithm which has a wide range of applications and better performance, and its search quaility is closely related with the parameters selection. Therefore, aiming at the large time-consuming problem of the existing improved ACO alogorithm, a novel ACO algorithm based on particle swarm optimization(PSO) algorithm is proposed. The new pheromone update method is presented, which combines the global asynchronous feature and elitist strategy. Moreover, the iteration number of ACO algorithm invoked by PSO algorithm is reduced significantly by large amounts of statistical experiments. The simulation results show that the proposed ACO algorithm has obvious advantage in search speed when it is used for solving the large-scale traveling salesman problem.