强社会认知能力的粒子群优化算法

Particle Swarm Optimization algorithm with abundant social cognition

针对粒子群优化算法的"早熟"问题,提出了强社会认知能力粒子群优化算法,该算法通过学习概率和选择概率确定粒子跟踪的局部极值。算法中学习概率的自适应调整有效权衡了粒子的个体认知能力和社会认知能力。通过经典函数的测试结果表明,新算法的全局搜索能力有了显著提高,并且能够有效避免早熟问题。

A particle swarm optimization with abundant social cognition is developed for solving premature convergence of particle swarm optimization.In this algorithm,the optimum from the particles experiments is determined by learning probability and selective probability.The learning probability is adjusted to balance between personal cognitive and social cognitive.Experimental results for complex function optimization show this algorithm improves the global convergence ability and efficiently prevents the algorithm from the local optimization and early maturation.