The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powe...The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.展开更多
为提高燃料电池并网发电系统运行的小干扰稳定性,提出一种燃料电池并网发电系统控制参数全局优化方法。针对大功率质子交换膜燃料电池(PEMFC)动态特性,建立150 k W的PEMFC发电系统模型,在此基础上建立系统的小信号模型。利用特征值分析...为提高燃料电池并网发电系统运行的小干扰稳定性,提出一种燃料电池并网发电系统控制参数全局优化方法。针对大功率质子交换膜燃料电池(PEMFC)动态特性,建立150 k W的PEMFC发电系统模型,在此基础上建立系统的小信号模型。利用特征值分析法分析确定影响系统稳定的关键参数,在充分考虑系统小干扰稳定性、阻尼比和稳定裕度协调优化情况下,利用回溯搜索算法(BSA)实现对燃料电池发电系统的关键控制参数的全局优化。展开更多
基金supported by the National Natural Science Foundation of China(61271250)
文摘The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.
文摘为提高燃料电池并网发电系统运行的小干扰稳定性,提出一种燃料电池并网发电系统控制参数全局优化方法。针对大功率质子交换膜燃料电池(PEMFC)动态特性,建立150 k W的PEMFC发电系统模型,在此基础上建立系统的小信号模型。利用特征值分析法分析确定影响系统稳定的关键参数,在充分考虑系统小干扰稳定性、阻尼比和稳定裕度协调优化情况下,利用回溯搜索算法(BSA)实现对燃料电池发电系统的关键控制参数的全局优化。
