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中文摘要：
群体多样性的丧失是导致粒子群优化（PSO）出现早期收敛的重要原因，鉴于此，对PSO运动方程进行概率特性分析，指出了方程中学习参数的概率分布及参数问的相依性与群体多样性丧失之间的关系，并提出了一种白适应学习的PSO算法．该算法通过调整学习参数的概率特性来保持种群多样性，同时设计了随进化状态白适应变化的学习参数来协调粒子的全局与局部搜索能力．实验结果表明，自适应学习的PSO算法提高了收敛的精度，有效避免了早期收敛．
英文摘要：
The loss of population diversity is an important reason to cause the premature convergence in particle swarm optimization（PSO）. Therefore, this paper makes the probabilistic characteristics analysis of learning parameters of PSO and proposes the relationship between the loss of population diversity and the probabilistic distribution and dependence of learning parameters. Then an adaptive learning particle swarm optimization（ALPSO） is proposed, where the modified probabilistic distribution of learning parameters is used to maintain population diversity. Meanwhile, the adaptive learning parameter with changing of evolutional state is designed to balance the global and local search abilities of particles. Experimental results show that ALPSO improves the convergence precision and effectivelY avoids the premature convergence.
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