微动粒子群优化算法用于Egun的多参量优化
Jiggle particle swarm optimization algorithm for multi-parameter optimization of Egun
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摘要: 针对2维电子光学多参量优化问题，采用微动粒子群优化算法，在给出目标电子轨迹和优化范围的前提下，可以得到趋近于该电子轨迹的真空边界和聚束磁结构。该算法分为前后两阶段：第一阶段采用前后试探法(微动)，同时参照最优粒子的信息；第二阶段采用标准粒子群优化算法。针对涉及多个相关参量的电子光学设计问题，标准粒子群优化算法仅能保证以较高概率收敛到局部最佳解，而微动粒子群优化算法能以较高概率收敛到全局最佳解，并且展现了多核计算机在电子光学设计上的潜力。初步的软件试验显示：消耗人类工程师几周时间的电子光学设计问题，用微动粒子群算法在普通个人计算机上几十小时就能完成。Abstract: A jiggle particle swarm optimization (JPSO) algorithm has been adopted for multi-parameter optimization of 2D electro-optical problems. Given the target track of electron trajectory and scope of optimization, it can find rotating axisymmetric vacuum boundaries and magnetic structures whose electron trajectories are close to the target one. The algorithm consists of two phases. In the first phase, up and down test method (jiggle) is adopted with continuous reference to the best particle’s information. In the second phase, the basic particle swarm optimization (PSO) algorithm is adopted. The JPSO algorithm can find the global best solution with higher probability than the basic PSO algorithm, and shows multi-core computer’s potential in engineering design of electro-optical problems. This
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