一种动态改变惯性权的自适应粒子群算法

针对惯性权值线性递减粒子群算法(LDW)不能适应复杂的非线性优化搜索过程的问题,提出了一种动态改变惯性权的自适应粒子群算法(DCW).在该算法中引入了参数粒子群进化速度因子和聚集度因子,并根据这2个参数对粒子群算法搜索能力的影响,将惯性因子表示为粒子群进化速度因子和聚集度因子的函数.在每次迭代时算法可根据当前粒子群进化速度因子和聚集度因子动态地改变惯性权值,从而使算法具有动态自适应性.对几种典型函数的测试结果表明,DCW算法的收敛速度明显优于LDW算法,收敛精度也有所提高.

Adaptive Particle Swarm Algorithm with Dynamically Changing Inertia Weight

A new particle swarm algorithm with dynamically changing inertia weight(DCW) is presented to solve the problem that the linearly decreasing weight(LDW) of the particle swarm algorithm cannot adapt to the complex and nonlinear optimization process.The evolution speed factor and aggregation degree factor of the swarm are introduced in this new algorithm and the weight is formulated as a function of these two factors according to their impact on the search performance of the swarm.In each iteration process,the weight is changed dynamically based on the current evolution speed factor and aggregation degree factor,which provides the algorithm with effective dynamic adaptability.The algorithms of LDW-PSO and DCW-PSO are tested with three well-known benchmark functions.The experiments show that the convergence speed of DCW-PSO is significantly superior to DCW-PSO,and the convergence accuracy is also increased.