Abstract: In order to improve robustness of the traditional adaptive particle swarm optimization algorithm, a novel adaptive algorithm which is called Cloud Adaptive Particle Swarm Optimization algorithm（CAPSO） is proposed. In the CAPSO, the inertia weight is adaptively varied depending on X-conditional cloud generator. CAPSO can improve its convergence capacity because of the stable tendency of cloud model. Meanwhile, it can remarkably avoid a local minimum using the randomness of cloud model to maintain diversity in the population. The performance of the CAPSO which is used for solving numerical integral of any function shows that the presented numerical integral algorithm has value in engineering practice.

Key words: cloud theory, Adaptive Particle Swarm Optimization algorithm（APSO）, Cloud Adaptive Particle Swarm Optimization algorithm（CAPSO）, numerical integral

摘要： 为了提高传统自适应粒子群优化算法的鲁棒性，由[X]条件云发生器自适应调整粒子的惯性权重，提出云自适应粒子群优化算法。由于云滴具有随机性和稳定倾向性的特点，使得惯性权重既具有传统的趋向性，满足快速寻优能力，又具有随机性，有利于提高种群的多样性，提高了收敛速度。通过对求解任意函数数值积分的实验表明，该算法计算精度高、求解速度快，是求解数值积分的一种有效的方法。

关键词: 云理论, 自适应粒子群优化算法, 云自适应粒子群优化算法, 数值积分