针对带时间窗的车辆路径问题(Vehicle Routing Problems with Time Windows, VRPTW),提出一种混合大规模领域搜索的改进蜣螂优化算法(Improved Dung Beetle Optimization of ALNS, ALSN-IDBO)进行求解。本文主要的改进点为:1) 设计新的...针对带时间窗的车辆路径问题(Vehicle Routing Problems with Time Windows, VRPTW),提出一种混合大规模领域搜索的改进蜣螂优化算法(Improved Dung Beetle Optimization of ALNS, ALSN-IDBO)进行求解。本文主要的改进点为:1) 设计新的编码解码方式实现连续蜣螂位置向量向离散客户序列的转化;2) 对于蜣螂优化算法的初始化采用随机、贪婪、最邻近而策略;3) 在ALNS中设计了3个移除算子和3个重插算子;4) 在传统的DBO中针对繁育的蜣螂和小蜣螂分别改进为螺旋搜索策略和三角游走策略。通过在标准Solomon数据集的部分算例进行实验,将本文算法与GA、DBO、ALNS算法进行对比,实验结果表明,本文所提出的混合大规模领域搜索的改进蜣螂优化算法能找到更好的解,并且寻优能力和稳定性均优于对比算法。展开更多
蜣螂优化算法(Dung Beetle Optimizer, DBO)是Xue等人在2022年提出的一种新的群体智能优化算法,其灵感来源于蜣螂的生物行为过程。针对蜣螂优化算法全局探索和局部开发能力不平衡、容易陷入局部最优等缺点,提出了一种混合策略改进的蜣...蜣螂优化算法(Dung Beetle Optimizer, DBO)是Xue等人在2022年提出的一种新的群体智能优化算法,其灵感来源于蜣螂的生物行为过程。针对蜣螂优化算法全局探索和局部开发能力不平衡、容易陷入局部最优等缺点,提出了一种混合策略改进的蜣螂优化算法(MIDBO)。首先,在种群初始化时,引入Tent混沌反向学习策略,使初始种群成员能够均匀分布,增加种群丰富性;其次,引入三角形随机游走策略改进繁殖蜣螂位置更新方式,平衡了全局搜索和局部挖掘能力;然后,加入动态权重系数改进蜣螂偷窃行为,加快算法的收敛速度;最后,引入混合变异算子对最优蜣螂位置进行扰动,提高算法跳出局部最优的能力。将所提算法与其他知名优化算法进行了15个基准测试函数的测试比较,仿真结果表明,MIDBO算法是可行有效的,其寻优精度和收敛速度都有了很大的提高,总体性能更好。Dung Beetle Optimizer (DBO) is a new swarm intelligence optimization algorithm proposed by Xue et al. in 2022, inspired by the biological behavior process of dung beetles. A mix-strategy improved dung beetle optimizer (MIDBO) is proposed to address the drawbacks of imbalanced global exploration and local development capabilities, as well as the tendency to fall into local optima. Firstly, during population initialization, a Tent chaotic reverse learning strategy is introduced to enable the initial population members to be evenly distributed and increase population richness;secondly, the introduction of triangle random walk strategy improves the position update method of breeding dung beetles, balancing global search and local mining capabilities;then, a hybrid mutation operator is adopted to improve the theft behavior of dung beetles and accelerate the convergence speed of the algorithm;finally, a mixed mutation operator is introduced to perturb the optimal dung beetle position, improving the algorithm’s ability to jump out of local optima. The proposed algorithm was compared with other well-known optimization algorithms through 15 benchmark test functions, and simulation results showed that the MIDBO algorithm is feasible and effective. Its optimization accuracy and convergence speed have been greatly improved, and the overall performance is better.展开更多
文摘蜣螂优化算法(Dung Beetle Optimizer, DBO)是Xue等人在2022年提出的一种新的群体智能优化算法,其灵感来源于蜣螂的生物行为过程。针对蜣螂优化算法全局探索和局部开发能力不平衡、容易陷入局部最优等缺点,提出了一种混合策略改进的蜣螂优化算法(MIDBO)。首先,在种群初始化时,引入Tent混沌反向学习策略,使初始种群成员能够均匀分布,增加种群丰富性;其次,引入三角形随机游走策略改进繁殖蜣螂位置更新方式,平衡了全局搜索和局部挖掘能力;然后,加入动态权重系数改进蜣螂偷窃行为,加快算法的收敛速度;最后,引入混合变异算子对最优蜣螂位置进行扰动,提高算法跳出局部最优的能力。将所提算法与其他知名优化算法进行了15个基准测试函数的测试比较,仿真结果表明,MIDBO算法是可行有效的,其寻优精度和收敛速度都有了很大的提高,总体性能更好。Dung Beetle Optimizer (DBO) is a new swarm intelligence optimization algorithm proposed by Xue et al. in 2022, inspired by the biological behavior process of dung beetles. A mix-strategy improved dung beetle optimizer (MIDBO) is proposed to address the drawbacks of imbalanced global exploration and local development capabilities, as well as the tendency to fall into local optima. Firstly, during population initialization, a Tent chaotic reverse learning strategy is introduced to enable the initial population members to be evenly distributed and increase population richness;secondly, the introduction of triangle random walk strategy improves the position update method of breeding dung beetles, balancing global search and local mining capabilities;then, a hybrid mutation operator is adopted to improve the theft behavior of dung beetles and accelerate the convergence speed of the algorithm;finally, a mixed mutation operator is introduced to perturb the optimal dung beetle position, improving the algorithm’s ability to jump out of local optima. The proposed algorithm was compared with other well-known optimization algorithms through 15 benchmark test functions, and simulation results showed that the MIDBO algorithm is feasible and effective. Its optimization accuracy and convergence speed have been greatly improved, and the overall performance is better.
