Abstract: To take advantage of different algorithms, a hybrid optimization algorithm is proposed based on the combination of Differential Evolution（DE） and Particle Swarm Optimization（PSO）. At the last period of the hybrid optimization, a new population will be produced around the best position found by the PSO, and DE is carried out with this population. The hybrid optimization can deduce the computational work to some degree and has more chance to find the best solution in a better region. Numerical tests on some benchmark functions are conducted for the algorithm evaluation. The results show the higher precision and more probability to find the best solution.

Key words: Differential Evolution（DE）, Particle Swarm Optimization（PSO）, hybrid algorithm

摘要： 利用粒子群算法的快速收敛性和差分进化算法的搜索精度较高等特点，提出了一种新的混合优化算法。该算法在粒子群算法的中后期，在已经寻找到的最优位置周围，随机生成一定数量的粒子进行差分进化算法，可以减少一定的运算量和在较优的区域进行寻找最优解。通过几个Benchmark函数的测试证明，新的混合算法具有搜索精度更高和更快收敛的优点。

关键词: 差分进化算法, 粒子群优化算法, 混合算法