基于仿生学改进的粒子群算法

Particle Swarm Optimization based on bionics

针对标准粒子群算法收敛速度慢和易陷入局部最优的局限性,提出了一种基于仿生学改进的粒子群算法。即通过在标准粒子群公式中加入负梯度项,使算法更加符合鸟群觅食的实际规律,同时使算法的全局和局部搜索能力得到了平衡。仿真对比结果表明,改进的粒子群算法减小了陷入局部极值的可能性,能够提高最优解的精度和优化效率。

The classic Particle Swarm Optimization has some deficiencies, such as falling in the local optimal region, slow convergence velocity, and so on. Aimed at these disadvantages an improved PSO algorithm is proposed. By employing the information about negative gradient to the standard particle swarm algorithm formula, an improved PSO algorithm can make the equilibrium more closed to the real rules of birds swarm’s foraging. At the same time, the global and local search ability of algorithm is balanced. Simulation results show that, an improved PSO algorithm reduces the chances of getting into the local extremum. At the same time, it can improve the solution accuracy of optimal solution and optimizing efficiency.