Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the chall...Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.展开更多
针对遗传算法全局搜索能力强和粒子群优化收敛速度快的特点,本文从种群个体组织结构上着手,进行优势互补,提出了一种遗传算法和粒子群优化的多子群分层混合算法(multi-subgroup hierarchical hybrid of genetic algorithm and particle ...针对遗传算法全局搜索能力强和粒子群优化收敛速度快的特点,本文从种群个体组织结构上着手,进行优势互补,提出了一种遗传算法和粒子群优化的多子群分层混合算法(multi-subgroup hierarchical hybrid of genetic algorithm and particle swarm optimization,HGA–PSO).算法采用分层结构,底层由一系列的遗传算法子群组成,贡献算法的全局搜索能力;上层是由每个子群的最优个体组成的精英群,采用钳制了初始速度的粒子群算法进行精确局部搜索.文中分析论证了HGA–PSO算法具有全局收敛性,并采用7个典型高维Benchmark函数进行测试,实验结果显示该算法的优化性能显著优于其他测试算法.展开更多
车辆路径问题旨在求解每辆车的服务路线,使其在完成配送任务的情况下行驶距离之和最短,是运筹学中经典的组合优化问题,属于NP难问题,且具有较高的理论意义与实际应用价值。针对该问题,提出了一种基于分层学习和差分进化的混合粒子群优...车辆路径问题旨在求解每辆车的服务路线,使其在完成配送任务的情况下行驶距离之和最短,是运筹学中经典的组合优化问题,属于NP难问题,且具有较高的理论意义与实际应用价值。针对该问题,提出了一种基于分层学习和差分进化的混合粒子群优化算法(Hybrid Particle Swarm Optimization Algorithm Based on Hierarchical Learning and Different Evolution,DEHSLPSO)。DE-HSLPSO中引入了分层学习策略,以适应度值和迭代次数为依据将种群粒子动态划分为3层,在前两层粒子的进化过程中引入了社会学习机制,而第三层粒子进行差分进化,通过变异和交叉有效地增加粒子的多样性,从而开拓空间,有利于跳出局部最优。通过在经典的CVRP数据集上进行仿真实验,来探究DE-HSLPSO各部分对整体性能的影响,实验证明分层策略与差分进化均可提升算法的整体性能。另外,在7个基本算例上对DE-HSLPSO与其他优化算法进行了测试,综合时间与最优解进行对比,结果表明DE-HSLPSO的求解性能优于其他对比算法。展开更多
基金jointly supported by the Jiangsu Postgraduate Research and Practice Innovation Project under Grant KYCX22_1030,SJCX22_0283 and SJCX23_0293the NUPTSF under Grant NY220201.
文摘Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.
文摘针对遗传算法全局搜索能力强和粒子群优化收敛速度快的特点,本文从种群个体组织结构上着手,进行优势互补,提出了一种遗传算法和粒子群优化的多子群分层混合算法(multi-subgroup hierarchical hybrid of genetic algorithm and particle swarm optimization,HGA–PSO).算法采用分层结构,底层由一系列的遗传算法子群组成,贡献算法的全局搜索能力;上层是由每个子群的最优个体组成的精英群,采用钳制了初始速度的粒子群算法进行精确局部搜索.文中分析论证了HGA–PSO算法具有全局收敛性,并采用7个典型高维Benchmark函数进行测试,实验结果显示该算法的优化性能显著优于其他测试算法.
文摘车辆路径问题旨在求解每辆车的服务路线,使其在完成配送任务的情况下行驶距离之和最短,是运筹学中经典的组合优化问题,属于NP难问题,且具有较高的理论意义与实际应用价值。针对该问题,提出了一种基于分层学习和差分进化的混合粒子群优化算法(Hybrid Particle Swarm Optimization Algorithm Based on Hierarchical Learning and Different Evolution,DEHSLPSO)。DE-HSLPSO中引入了分层学习策略,以适应度值和迭代次数为依据将种群粒子动态划分为3层,在前两层粒子的进化过程中引入了社会学习机制,而第三层粒子进行差分进化,通过变异和交叉有效地增加粒子的多样性,从而开拓空间,有利于跳出局部最优。通过在经典的CVRP数据集上进行仿真实验,来探究DE-HSLPSO各部分对整体性能的影响,实验证明分层策略与差分进化均可提升算法的整体性能。另外,在7个基本算例上对DE-HSLPSO与其他优化算法进行了测试,综合时间与最优解进行对比,结果表明DE-HSLPSO的求解性能优于其他对比算法。
