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针对群智能优化算法求解高维多峰函数难以优化粒子每一维和易陷入局部极值点问题,在分析量子行为粒子群优化(QPSO)算法机理的基础上,对QPSO算法进行改进,采取前后代粒子逐维对比优化,并构造一种新的调控收缩扩张系数函数.实验结果表明,改进算法在收敛精度和收敛速度上明显优于QPSO算法,具有很强的避免陷入局部最优的能力,非常适合求解高维、多峰优化问题.
In order to solve the problem that the high dimensional and multimodal functions of swarm intelligence optimization algorithm can not optimize each dimension of a particle and easily fall into local extremum points, QPSO algorithm is improved based on the analysis of QPSO algorithm. The particles are optimized one by one by dimension and a new function of contraction-expansion coefficient is constructed.The experimental results show that the improved algorithm is superior to QPSO algorithm in convergence accuracy and convergence speed, and has strong ability to avoid falling into local optimum. Suitable for solving high-dimensional, multi-peak optimization problems.