融合改进Tent混沌和模拟退火的灰狼算法

Grey Wolf Algorithm Based on Improved Tent Chaos and Simulated Annealing

针对标准灰狼算法种群多样性差、后期收敛速度慢、易陷入局部最优的缺陷,提出一种改进灰狼算法.利用改进Tent混沌映射初始化种群,增加种群多样性;引入螺旋函数,提高算法收敛速度;融合模拟退火思想,避免陷入局部最优;设置搜索阈值,平衡全局搜索与局部搜索;利用改进Tent混沌映射产生新个体,替换性能较差个体并进行高斯扰动,增加寻优精度;将当前解和新解进行算术杂交,以保留当前解优点并减小扰动差异.使用基准测试函数和共享单车停车点选址及期初配置模型测试算法性能.结果表明,改进灰狼算法较标准灰狼算法、遗传算法和粒子群算法,收敛速度更快,寻优精度更高,性能更优越,并将该算法应用到共享单车停车选址上,验证了算法的有效性.

In view of the shortcomings of standard grey Wolf algorithm,such as poor population diversity,slow convergence rate and easy to fall into local optimum,an improved grey Wolf algorithm was proposed.The improved Tent chaotic map was used to initialize the population and increase its diversity.The helical function was introduced to improve the convergence speed of the algorithm.The idea of simulated annealing was fused to avoid falling into local optimality;Setted the search threshold to balance global search and local search;The improved Tent chaotic map was used to generate new individuals,replaced the ones with poor performance and carried out Gaussian perturbation to increase the optimization accuracy.In order to preserve the advantages of the current solution and reduce the perturbation differences,the current solution and the new solution were numerically hybridized.The performance of the algorithm was tested by using the benchmark function and the model of Shared bike parking spot location and initial configuration.The results show that the improved grey Wolf algorithm has faster convergence speed,higher optimization precision and better performance than the standard grey Wolf algorithm,genetic algorithm and particle swarm optimization algorithm.The application of the improved grey Wolf algorithm to the parking site selection of Shared bikes verifies the effectiveness of the algorithm.