一种邻居动态调整的粒子群优化算法

为了达到全局寻优能力与寻优速度的平衡，提出一种邻居动态调整的粒子群优化算法。该算法依据粒子的多样性变化和进化状态，实现邻居结构的动态改变。算法引入种群熵评估粒子的多样性，定义粒子邻居扩充因子和局部影响因子来描述粒子的进化状态，并提出邻居扩充与约束策略来控制好粒子的影响力。实验结果表明，该算法具有较强的全局寻优能力和较好的寻优速度。

Particle Swarm Optimization Algorithm Using Dynamic Neighborhood Adjustment

To keep a balance between global searching ability and searching speed, a particle swarm optimization algorithm using dynamic neighborhood adjustment (PSODNA) is presented. According to swarm diversity variation and evolutionary state, neighborhood structure of the particle swarm is dynamically changed in PSODNA. Population entropy is introduced to estimate swarm diversity. Particle neighborhood extension factor and local effect factor are defined to describe the evolutionary state. And neighborhood expansion and constraint strategies are proposed to control the influence of good particles. The experimental results show that the proposed algorithm has great superiority both in global searching ability and searching speed.