基于鸽群算法的量子融合算法

Quantum Fusion Algorithm Based on Pigeon-inspired Optimization

对于鸽群算法存在的过早收敛问题,提出了一种新的改进算法。该算法采用反向学习法进行初始化设置,在引入量子计算规则的同时融合鱼群算法,在迭代过程中采用模拟退火方式选取全局极值,逐步向最优解靠近。将改进的融合算法应用于函数优化方面,用多个测试函数的求解来评价算法性能。实验结果表明,新算法能快速搜索到问题的全局最优值,在求解高精度问题时的表现也较为优秀,有效地改善了过早收敛问题,提高了算法性能。

For the premature convergence problem of pigeon flock algorithm,a new improved algorithm is proposed.The algorithm adopts the reverse learning method to initialize the settings,introduces quantum computing rules and integrates the fish swarm algorithm.In the iterative process,the simulated annealing method is used to select the global extreme value and gradually approach the optimal solution.The improved fusion algorithm is applied to function optimization,and the algorithm performance is evaluated by solving several test functions.The experimental results show that the new algorithm can quickly search for the global optimal value of the problem,and it also performs well in solving high-precision problems,effectively improving the premature convergence problem and improving the performance of the algorithm.