一种新的自适应动态文化粒子群优化算法

New adaptive dynamic cultural particle swarm optimization algorithm

为了克服粒子群优化算法在解决复杂问题时易陷入局部最优的缺陷,提出了一种新的自适应动态文化粒子群优化算法。该算法引入评价粒子群早熟收敛程度的指标来判断种群空间粒子群状态,以确定影响函数对种群空间粒子群的作用时机,当算法陷入局部最优时,自适应地利用影响函数对种群空间进行变异更新,从而有效发挥文化粒子群算法的双演化双促进机制。并且根据种群的早熟收敛程度自适应地调整粒子的惯性权重,使种群在进化过程中始终保持惯性权重的多样性,在算法的全局收敛性与收敛速度之间作一个很好的折中。最后对四个经典的测试函数进行仿真,结果表明该算法具有很强的搜索能力,收敛速度和收敛精度也有所提高。

In order to avoid particle swarm optimization algorithm easy to fall into local optimum in solving complex problems, this paper proposed a new adaptive dynamic cultural particle swarm optimization algorithm. It introduced the evaluation of particle swarm premature convergence indicators into population space. By calculating the evaluation of particle swarm premature convergence indicators, decisions whether to have mutated operation on population space. It made the improved algorithm could make better use of mechanism of dual evolution and dual promotion in cultural particle swarm optimization algorithm. It adjusted the inertia weight of the particle adaptively based on the premature convergence degree of the swarm. The diversity of inertia weight made a compromise between the global convergence and convergence speed. It tested the proposed algorithm with four well-known benchmark functions. The experimental results show that the new algorithm has great global search ability convergence accuracy and convergence velocity is also increased and avoid the premature convergence problem effectively.