中文摘要：

针对粒子群优化算法在求解高维问题时易出现的早熟收敛、停滞现象，提出一种拟随机初始化模拟退火粒子群算法。采用Hammersley方法对算法进行初始化，可以提高算法在高维搜索空间的搜索能力，进一步将模拟退火思想引入到粒子群优化算法中，结合粒子群优化算法的快速寻优能力和模拟退火算法的概率突跳特性，使算法具有跳出局部最优从而实现全局最优的能力。分别在5个经典测试函数上测试算法的性能，仿真实验结果表明，提出的算法有效克服了传统粒子群优化算法在求解高维空间优化问题时易出现的停滞现象，在进化后期仍保持较强的搜索能力，提高了传统粒子群优化算法在高维空间的全局寻优能力。

英文摘要：

To overcome the shortcomings of particle swarm optimization （ PSO） algorithm such as prema-ture convergence and stagnation when solving the high-dimensional problems, a quasi-randomized simula-ted annealing （ SA）-PSO algorithm was proposed. The performance of algorithm in high-dimensional opti-mization space could be improved by using the Hammersley initialization. And the idea of SA algorithm was introduced into the PSO algorithm, combining with the fast searching ability of PSO and the probabi-listic jumping property of SA, to jump out of local optimal algorithm to achieve the global optimum. The proposed algorithm could effectively overcome the stagnation phenomenon, enhance the global search ability in high-dimensional space. The proposed algorithm was then tested on 5 different functions, and the results demonstrated better optimization ability over the traditional PSO algorithm.