Multi-objective optimal dispatching schemes with intelligent algorithms are recognized as effective measures to promote the economics and environmental friendliness of microgrid applications.However,the low accuracy a...Multi-objective optimal dispatching schemes with intelligent algorithms are recognized as effective measures to promote the economics and environmental friendliness of microgrid applications.However,the low accuracy and poor convergence of these algorithms have been challenging for system operators.The bird swarm algorithm(BSA),a new bio-heuristic cluster intelligent algorithm,can potentially address these challenges;however,its computational iterative process may fall into a local optimum and result in premature convergence when optimizing small portions of multi-extremum functions.To analyze the impact of a multi-objective economic-environmental dispatching of a microgrid and overcome the aforementioned problems of the BSA,a self-adaptive levy flight strategy-based BSA(LF-BSA)was proposed.It can solve the dispatching problems of microgrid and enhance its dispatching convergence accuracy,stability,and speed,thereby improving its optimization performance.Six typical test functions were used to compare the LF-BSA with three commonly accepted algorithms to verify its excellence.Finally,a typical summer-time daily microgrid scenario under grid-connected operational conditions was simulated.The results proved the feasibility of the proposed LF-BSA,effectiveness of the multi-objective optimization,and necessity of using renewable energy and energy storage in microgrid dispatching optimization.展开更多
Bird swarm algorithm(BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimi...Bird swarm algorithm(BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimization problems. To enhance the performance of BSA, handling boundary constraints are applied to fix the candidate solutions that are out of boundary or on the boundary in iterations, which can boost the diversity of the swarm to avoid the premature problem. On the other hand, we accelerate the foraging behavior by adjusting the cognitive and social components the sin cosine coefficients. Simulation results and comparison based on sixty benchmark functions demonstrate that the improved BSA has superior performance over the BSA in terms of almost all functions.展开更多
计算密集型任务数量的增加导致智能移动设备(Smart Mobile Devices,SMD)计算任务过载,借助MEC(Mobile Edge Computing Servers)及利用网络中空闲边缘设备(Edge Devices,ED)可使计算能力受限的SMD将计算任务卸载到MEC和ED协作中,并基于...计算密集型任务数量的增加导致智能移动设备(Smart Mobile Devices,SMD)计算任务过载,借助MEC(Mobile Edge Computing Servers)及利用网络中空闲边缘设备(Edge Devices,ED)可使计算能力受限的SMD将计算任务卸载到MEC和ED协作中,并基于委托信誉证明(Delegated Proof of Reputation,DPoR)共识机制增强系统的安全性。文中提出一种基于鸟群人工鱼群算法(Bird Swarm-Artificial Fish Swarm Algorithm,BS-AFSA)的区块链移动边缘计算卸载模型,将任务卸载问题转化为优化目标函数来降低计算开销。采用改进鸟群人工鱼群算法来优化任务时延和能量消耗,对算法中的行为参数进行针对性构造,并改进拥挤度因子来提高后期迭代中寻优的局部搜索精度。仿真结果表明,与其他基准算法相比,文中所提算法减少了陷入局部最优的可能性,并降低了联合卸载方案的系统总开销。展开更多
针对特征权重难以准确量化的问题,提出一种基于灰狼优化(grey wolf optimizer, GWO)算法和鸟群算法(bird swarm algorithm, BSA)的混合算法,用于特征权重的寻优。首先,将Chebyshev映射、反向学习与精英策略用于混合算法的初始种群生成;...针对特征权重难以准确量化的问题,提出一种基于灰狼优化(grey wolf optimizer, GWO)算法和鸟群算法(bird swarm algorithm, BSA)的混合算法,用于特征权重的寻优。首先,将Chebyshev映射、反向学习与精英策略用于混合算法的初始种群生成;其次,将改进后的GWO算法位置更新策略融入BSA的觅食行为中,得到一种新的局部搜索策略;然后,将BSA的警觉行为与飞行行为用作混合算法的全局搜索平衡策略,从而得到一种收敛的灰狼-鸟群算法(grey wolf and bird swarm algorithm, GWBSA),通过GWBSA的迭代寻优可获得各特征的权重值。利用标准测试函数和标准分类数据集进行了对比实验,与遗传算法、蚁狮算法等方法相比,GWBSA具有较快的收敛速度且不易陷入局部最优,可以提高模式分类问题的求解质量。展开更多
针对鸟群优化算法迭代初期种群多样性不足、迭代后期收敛速度慢、易陷入局部最优解等问题,提出一种融合柯西变异的鸟群与算术混合优化算法(hybrid algorithm of bird swarm algorithm and arithmetic optimization algorithm based on C...针对鸟群优化算法迭代初期种群多样性不足、迭代后期收敛速度慢、易陷入局部最优解等问题,提出一种融合柯西变异的鸟群与算术混合优化算法(hybrid algorithm of bird swarm algorithm and arithmetic optimization algorithm based on Cauchy mutation,HBSAAOA)。利用算术优化算法中乘除算子的高分布性对BSA中生产者的位置进行更新,以提高种群多样性,增强全局搜索能力。引入随机搜索策略和柯西变异策略来生成候选解,对后期局部开发阶段进行扰动,以增强算法跳出局部最优解的能力并提高收敛速度。利用贪婪策略对最优个体进行选择并替代较差的个体,从而提高解的质量。通过对23个经典测试函数以及部分CEC2014基准函数进行仿真实验,并将HBSAAOA应用到两个工程应用问题上,结果表明改进策略有效,改进算法的收敛速度更快、寻优精度更高,并且鲁棒性更好。展开更多
基金supported by the National Natural Science Foundation of China (No. 52061635103)
文摘Multi-objective optimal dispatching schemes with intelligent algorithms are recognized as effective measures to promote the economics and environmental friendliness of microgrid applications.However,the low accuracy and poor convergence of these algorithms have been challenging for system operators.The bird swarm algorithm(BSA),a new bio-heuristic cluster intelligent algorithm,can potentially address these challenges;however,its computational iterative process may fall into a local optimum and result in premature convergence when optimizing small portions of multi-extremum functions.To analyze the impact of a multi-objective economic-environmental dispatching of a microgrid and overcome the aforementioned problems of the BSA,a self-adaptive levy flight strategy-based BSA(LF-BSA)was proposed.It can solve the dispatching problems of microgrid and enhance its dispatching convergence accuracy,stability,and speed,thereby improving its optimization performance.Six typical test functions were used to compare the LF-BSA with three commonly accepted algorithms to verify its excellence.Finally,a typical summer-time daily microgrid scenario under grid-connected operational conditions was simulated.The results proved the feasibility of the proposed LF-BSA,effectiveness of the multi-objective optimization,and necessity of using renewable energy and energy storage in microgrid dispatching optimization.
基金Supported by the National Natural Science Foundation of China(11871383,71471140 and 11771058)
文摘Bird swarm algorithm(BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimization problems. To enhance the performance of BSA, handling boundary constraints are applied to fix the candidate solutions that are out of boundary or on the boundary in iterations, which can boost the diversity of the swarm to avoid the premature problem. On the other hand, we accelerate the foraging behavior by adjusting the cognitive and social components the sin cosine coefficients. Simulation results and comparison based on sixty benchmark functions demonstrate that the improved BSA has superior performance over the BSA in terms of almost all functions.
文摘计算密集型任务数量的增加导致智能移动设备(Smart Mobile Devices,SMD)计算任务过载,借助MEC(Mobile Edge Computing Servers)及利用网络中空闲边缘设备(Edge Devices,ED)可使计算能力受限的SMD将计算任务卸载到MEC和ED协作中,并基于委托信誉证明(Delegated Proof of Reputation,DPoR)共识机制增强系统的安全性。文中提出一种基于鸟群人工鱼群算法(Bird Swarm-Artificial Fish Swarm Algorithm,BS-AFSA)的区块链移动边缘计算卸载模型,将任务卸载问题转化为优化目标函数来降低计算开销。采用改进鸟群人工鱼群算法来优化任务时延和能量消耗,对算法中的行为参数进行针对性构造,并改进拥挤度因子来提高后期迭代中寻优的局部搜索精度。仿真结果表明,与其他基准算法相比,文中所提算法减少了陷入局部最优的可能性,并降低了联合卸载方案的系统总开销。
文摘针对特征权重难以准确量化的问题,提出一种基于灰狼优化(grey wolf optimizer, GWO)算法和鸟群算法(bird swarm algorithm, BSA)的混合算法,用于特征权重的寻优。首先,将Chebyshev映射、反向学习与精英策略用于混合算法的初始种群生成;其次,将改进后的GWO算法位置更新策略融入BSA的觅食行为中,得到一种新的局部搜索策略;然后,将BSA的警觉行为与飞行行为用作混合算法的全局搜索平衡策略,从而得到一种收敛的灰狼-鸟群算法(grey wolf and bird swarm algorithm, GWBSA),通过GWBSA的迭代寻优可获得各特征的权重值。利用标准测试函数和标准分类数据集进行了对比实验,与遗传算法、蚁狮算法等方法相比,GWBSA具有较快的收敛速度且不易陷入局部最优,可以提高模式分类问题的求解质量。
文摘针对鸟群优化算法迭代初期种群多样性不足、迭代后期收敛速度慢、易陷入局部最优解等问题,提出一种融合柯西变异的鸟群与算术混合优化算法(hybrid algorithm of bird swarm algorithm and arithmetic optimization algorithm based on Cauchy mutation,HBSAAOA)。利用算术优化算法中乘除算子的高分布性对BSA中生产者的位置进行更新,以提高种群多样性,增强全局搜索能力。引入随机搜索策略和柯西变异策略来生成候选解,对后期局部开发阶段进行扰动,以增强算法跳出局部最优解的能力并提高收敛速度。利用贪婪策略对最优个体进行选择并替代较差的个体,从而提高解的质量。通过对23个经典测试函数以及部分CEC2014基准函数进行仿真实验,并将HBSAAOA应用到两个工程应用问题上,结果表明改进策略有效,改进算法的收敛速度更快、寻优精度更高,并且鲁棒性更好。
