一种求解小规模CVRP问题的新型斑点鬣狗智能算法

New spotted hyena intelligent algorithm for solving small-scale CVRP problem

针对带容量约束的车辆路径问题(CVRP)的特性,提出一种融合多策略改进的新型斑点鬣狗智能优化(ISHO)算法.利用K-means聚类和贪心选择相结合的初始化方法.在标准斑点鬣狗算法的基础上,融入鲸鱼优化算法中螺旋探索机制,增强算法全局搜索能力;同时,引入一种变异算子、两种交叉算子与逆操作方法,将螺旋探索与交叉变异算子相结合,重新定义离散域中的斑点鬣狗算法.最后,将ISHO算法在4种不同类型共24个国际基准算例的计算结果与其他智能优化算法进行实验对比分析,结果表明:ISHO算法能够有效避免陷入局部最优和低质量解等缺点,对求解小规模CVRP问题有一定优越性.

A new spotted hyena intelligent optimizer(ISHO)algorithm incorporating multi-strategy improvement was proposed for the characteristics of the capacitated vehicle routing problem(CVRP).A combination of K-means clustering and greedy selection was used for the initialization method.Based on the standard spotted hyena optimizer,the spiral exploration mechanism in the whale optimization algorithm was incorporated to enhance the global search capability of the algorithm.Meanwhile,a variational operator,two crossover operators and reverse operation methods were proposed.The spiral exploration was combined with the cross variational operator to redefine the spotted hyena optimizer in discrete domains.Finally,the computational results of ISHO in four different types of a total of 24 international benchmark cases were experimentally compared and which was analyzed with other intelligent optimization algorithms.The results show that the ISHO algorithm effectively avoids the drawbacks of falling into local optimum and low quality solutions,and has certain superiority for solving small-scale CVRP problems.