优化复杂函数的粒子群-鸽群混合优化算法

Hybrid Particle Swarm Optimization and Pigeon-Inspired Optimization Algorithm for Solving Complex Functions

针对复杂函数优化问题,提出一种两阶段混合优化算法。对基本粒子群和鸽群算法进行改进,引入惯性因子和跳跃算子增强了粒子群算法的搜索能力,提出干扰算子增加了鸽群算法的种群多样性。将改进后的两种算法相结合,形成两阶段混合优化算法,同时定义了一种多样性函数对种群进行实时监测,以保证种群的多样性。采用两组经典测试函数,对算法性能进行测试。结果表明,算法适用于求解复杂函数优化问题,且具有较好的收敛速度和收敛精度。

In order to solve the complex function optimization problems, a two-stage hybrid optimization algorithm is proposed. The basic particle swarm optimization and pigeon-inspired optimization algorithm are improved. The inertia factor and jump operator are used to enhance the searching ability of particle swarm optimization, and the interference operator is used to increase the population diversity of pigeon-inspired optimization. The improved algorithm is combined to form the two-stage hybrid optimization algorithm. Meanwhile, a diversity function is defined to detect the population diversity in real-time monitoring to ensure the diversity of the population. The simulation results show that the algorithm is suitable for solving complex function optimization problems, and has good convergence speed and convergence accuracy.