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
随着无人车、无人机等自主移动机器人的井喷式发展,寻路算法的重要性也一再提升。跳点搜索+(jump point search plus,JPS+)算法因其显著的高搜索效率而成为了经典的静态栅格化地图寻路算法。对JPS+算法的改进策略和应用场景层出不穷,但...随着无人车、无人机等自主移动机器人的井喷式发展,寻路算法的重要性也一再提升。跳点搜索+(jump point search plus,JPS+)算法因其显著的高搜索效率而成为了经典的静态栅格化地图寻路算法。对JPS+算法的改进策略和应用场景层出不穷,但对路径长度与路径节点数量的优化方法仍有待研究。提出了一种对于强制跳点按功能性的分类方式,并基于这一分类对JPS+算法的预处理和搜索流程进行改进,在提高单次搜索扩展效率的同时,减少路径的长度与节点数。通过仿真地图实验与真实采样地图实验,验证了改进算法的有效性。对比发现:改进JPS+算法在仿真地图中,所求路径长度最大减少5.92%,路径节点数最大减少46.15%,算法用时最大减少25.58%;在真实采样地图中,所求路径长度平均减少2.48%,路径节点数平均减少10.71%,算法用时平均减少17.08%。展开更多
In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm （BSA） and sequential quadratic programming （SQP）. BSA, as an exploration search engine, gives a...In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm （BSA） and sequential quadratic programming （SQP）. BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.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.
文摘随着无人车、无人机等自主移动机器人的井喷式发展,寻路算法的重要性也一再提升。跳点搜索+(jump point search plus,JPS+)算法因其显著的高搜索效率而成为了经典的静态栅格化地图寻路算法。对JPS+算法的改进策略和应用场景层出不穷,但对路径长度与路径节点数量的优化方法仍有待研究。提出了一种对于强制跳点按功能性的分类方式,并基于这一分类对JPS+算法的预处理和搜索流程进行改进,在提高单次搜索扩展效率的同时,减少路径的长度与节点数。通过仿真地图实验与真实采样地图实验,验证了改进算法的有效性。对比发现:改进JPS+算法在仿真地图中,所求路径长度最大减少5.92%,路径节点数最大减少46.15%,算法用时最大减少25.58%;在真实采样地图中,所求路径长度平均减少2.48%,路径节点数平均减少10.71%,算法用时平均减少17.08%。
基金Acknowledgements This work was supported by the NSFC-Guangdong Joint Fund （U1201258）, the National Natural Science Foundation of China （Grant No. 61573219）, the Shandong Natural Science Funds for Distinguished Young Scholars （JQ201316）, the Fundamental Research Funds of Shandong University （2014JC028）, and the Natural Science Foundation of Fujian Province of China （2016J01280）.
文摘In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm （BSA） and sequential quadratic programming （SQP）. BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.
