新型粒子群优化算法

现有粒子群优化存在局部收敛、对可调参数敏感等缺点.基于此,本文提出一种新型粒子群优化算法.首先,通过分析社会个体对其环境的认知规律,简化粒子更新公式使粒子位置的更新仅与粒子自身速度及其邻域内最优粒子位置相关.其次,基于粒子速度划分提出一种优势粒子速度小概率变异、劣势速度随机赋值方法.最后,通过优化4个典型测试函数验证了本文所提方法在优化解的质量、算法收敛速度及鲁棒性等方面的优异性能.

Novel particle swarm optimization algorithm

Existing particle swarm optimization has disadvantages of local convergence and being sensitive to adjustable parameters. A novel particle swarm optimization algorithm is proposed to avoid the above disadvantages in this paper. Firstly, the formula for updating particles is simplified by analyzing the cognition rule of individuals to their environment, the update of a particle location is only related to its own velocity and the optimal particle location in its neighborhood. Secondly, strategies of mutation for superior particle velocities with a small probability and the random evaluation for inferior particle velocities are presented based on partition of particle velocities. Finally, the significant performance in quality of the optimal solutions, convergence speed and robustness of algorithm proposed in this paper are validated by optimizing four benchmark functions.