具有量子行为的粒子群优化算法惯性权重研究及应用

Application and Study on Inertia Weight in Particle Swarm Optimization with Quantum Behavior

在研究惯性权重对基本PSO算法影响的基础上,根据惯性权重对粒子群算法影响的特点,采用4种惯性权重策略对一种新的具有量子行为的粒子群算法的速度进行调节,比较每种算法的性能,从中找到一种新的性能更好的改进算法,将其用于求解0-1背包问题。实验结果表明较好地选择惯性权重参数对算法的性能有很大提高,该改进算法在求解0-1背包问题中具有高效性,提高了最优解的精度,同时具有较快的收敛速度。

The effect of inertia weight on Particle Swarm Optimization（PSO） is studied,on basis of which adopts four kinds of strategies of inertia weight to regulate the speed of a new Quantum Delta Potential Well based Particle Swarm Optimization （QDPSO）. A faster and more stabile algorithm, found by comparing the performances of four equations regulated the inertia weight,solves 0/1 knapsack problem. The result of experiment shows that the modified algorithm improves the precision of optimal solution and has a faster speed and a higher efficiency in convergence. In a word, choosing a parameter of inertia weight suitably can improve the performance of new QDPSO.