Compared with genetic algorithm and chaotic optimization, PSO is better in algorithmic structure, computing time, search area control, convergence speed and so on. The application is effective. 分析结果表明,PSO算法较之常用的遗传算法和混沌优化等算法,在算法结构、计算时间、搜索区间控制以及收敛速度等方面具有较好的特性,验证了该方法的有效性。
A new elicitation method of searching algorithmic convergence proof 一种新的启发式搜索算法的收敛性证明
This algorithm combines simulated annealing algorithm with genetic algorithm in the treatment of constraints and the selection of crossover and mutation probability as well as the mutation individuals, for the purpose of further improving the algorithmic searching space, searching efficiency and convergence performance. 该算法在遗传算法的约束条件处理、交叉和变异概率选取、变异个体等环节引入了模拟退火机制,实现了模拟退火和遗传算法的融合,进一步改善算法的搜索能力、搜索效率和收敛性能。
The algorithm only is capable to run into local solutions. For keeping population diversity, the algorithm needs enhance population scale, then with population scale expansion, algorithm searching is to be deferred and algorithmic convergence speed is to be affected. 要保持种群的多样性,还可以增大种群规模,可随着种群规模的扩大,将导致算法延迟,影响算法的收敛速度。