【摘要】 微粒群算法是一种新型的群智能算法,已被广泛用于各种复杂优化问题的求解,但算法依然面临着过早收敛问题.为克服算法的早熟问题,提出了自组织微粒群算法.将微粒群体视为自组织系统,引入负反馈机制.群体多样性是影响微粒群算法全局优化性能的关键因素,把群体多样性作为个体微粒可感知的群体动态信息,用于动态调整惯性权重或加速度系数,通过不同的特性参数实现微粒的集聚或分散,使群体维持适当的多样性水平以利于全局搜索.用于复杂函数优化问题的求解,并与其他典型改进算法进行了性能比较.仿真结果表明,基于多样性控制的自组织微粒群算法可以有效避免早熟问题,提高微粒群算法求解复杂函数的全局优化性能.更多还原

【Abstract】 Particle swarm optimization(PSO)is a novel swarm intelligence algorithm inspired by certain social behavior of bird flocking originally.Since proposed in 1995,the algorithm proved to be a valid optimization technique and has been applied in many areas successfully.However,like others evolutionary algorithms,PSO also suffers from the premature convergence problem,especially for the large scale and complex problems.In order to alleviate the premature convergence problem,the paper develops a self-organized PSO(SOPSO).SOPSO regards the swarm as a self-organized system,and introduces negative feedback mechanism to imitate the information interaction between the particles and the swarm background.Considering swarm diversity is a key factor influencing the global performances of PSO,SOPSO adopts swarm diversity as main dynamic information to control the tuning of parameters through feedback,which in turn can modify the particles to diverge or converge adaptively and contribute to a successful global search.The proposed methods are applied to some complex function optimizations and compared with the other notable improved PSO.Simulation results show SOPSO based on feedback control of swarm diversity is a feasible technique,which can alleviate the premature convergence validly and improve the global performances of PSO in solving the complex functions.更多还原