以旅行商问题为例,提出一种基于元胞结构的伪贪婪离散粒子群优化算法.为了体现粒子对环境的感知能力,设计了伪贪婪的粒子位置修改操作算子,为了反映粒子间不同学习能力,体现粒子的个体差异性,设计了3种学习算子来提高算法的局部求精能力,为了更好地保持粒子群的多样性,采用了元胞结构作为粒子群的种群拓扑和邻城结构,这些策略使算法在空间探索和局部求精间取得较好的平衡.在典型旅行商问题上进行了仿真,结果表明算法具有良好的性能.%A cellular structure based pseudo greedy discrete particle swarm optimization algorithm is designed to tackle the Traveling Salesman Problem. In order to express the environment cognitive capability of particle, a pseudo greedy position update operator is designed. In order to express different learning ability and individual differences among particles, three different learning operators are defined. In order to keep the diversity of particle swarm, cellular structure is used as the population topological and neighborhood structure. Using those strategies, the proposed algorithm can get good balance between exploration and exploitation. Simulations were carried on typical Traveling Salesman Problems from the literature. The simulation results show that it can produce good results.
展开▼
