【摘要】 遗传算法具有很强的自适应性、鲁棒性和全局搜索能力,但其局部搜索能力相对较弱,计算后期易出现进化缓慢、过早收敛等问题,蚁群算法是近几年迅速发展起来的一种新的全局优化算法,具有正反馈机制,但是计算初期由于信息素差别小,初始收敛速度较慢.本文将这两种优化方法结合起来,充分发挥各自的优势,形成了遗传-蚁群混合算法,并选用测试函数对算法的优化性能作了对比计算,最后以高温超导匀场磁体为实际应用目标,以绕制磁体所用超导带长度为目标函数对磁体结构进行优化设计,优化方案比原始方案节省7.32%的超导带材用量.更多还原

【Abstract】 Genetic algorithm (GA) is a popular global optimization method using the concept of natural evolution. It has such disadvantages as premature convergence, low convergence speed. Ant colony optimization (ACO) is a novel simulating evolution algorithm. It has the characteristic of positive feedback. But the convergence speed of ACO is lower at the beginning for there is only little pheromone difference on the path at that time. This paper introduced the principium, characteristics of ACO and combined it with genetic algorithm. The hybrid algorithm has both advantages of these two algorithms and the optimization efficiency was enhanced. The algorithm was used for the optimized design of high temperature superconductivity (HTS) magnet with highly uniform magnetic fields. The total length of HTS tape is reduced 7.32% for the optimized magnet.更多还原