中文摘要：

为解决骨干粒子群优化（Bare-Bone Particle Swarm Optimization，BBPSO）的早期收敛问题，本文通过粒子的运动行为分析了导致BBPSO早期收敛的因素，并提出并行协作BBPSO，该算法采用并行的主群和从群之间的协作学习来平衡勘探和开采能力。为了增强主群的勘探能力，提出动态学习榜样策略以保持群体多样性；同时提出随机反向学习机制以实现从群的从全局到局部的自适应搜索功能。在14个不同特征的测试函数上将本文算法与6种知名的BBPSO算法进行对比，仿真结果和统计分析表明本文算法在收敛速度和精度上都有显著提高。

英文摘要：

To deal with the premature convergence of the bare-bone particle swarm optimization （BBPSO）algo-rithm,we make the analysis of the motion behavior of the particles and point out the reasons leading to the premature con-vergence.According to the analysis results,a parallel-cooperative BBPSO （PCBBPSO）algorithm is proposed in which the parallel-cooperative learning of a master swarm and a slave swarm balances between exploration and exploitation abilities.In order to improve the exploration ability of the master swarm,a dynamic learning exemplar strategy is presented to preserve the swarm diversity.Meanwhile,a stochastic opposition-based learning mechanism is developed to achieve the abilities of the slave swarm from the global search to the local search.The proposed algorithm was evaluated on 14 benchmark functions with different characteristics.The experimental results and statistic analysis show that the proposed method significantly out-performs six state-of-the-art BBPSO variants in terms of convergence speed and solution accuracy.