机器人学是现在及未来科技发展的重点,路径规划是机器人学中的一个重要课题.生物界一些群居动物有严格的等级制度和职责分工,受社会群居动物行为启发,提出社会群体搜索算法(social group search algorithm,SGSO).社会群体搜索算法对群...机器人学是现在及未来科技发展的重点,路径规划是机器人学中的一个重要课题.生物界一些群居动物有严格的等级制度和职责分工,受社会群居动物行为启发,提出社会群体搜索算法(social group search algorithm,SGSO).社会群体搜索算法对群体的分类及信息反馈机制——领导-追随机制的制定,降低了早熟的概率,交叉变异和淘汰机制的引入增加了搜索范围,减少了陷入局部最优的可能.同时,对提出的社会群体搜索算法进行了分析,从理论上证明了算法的收敛性;将社会群体搜索算法应用于机器人路径规划进行仿真,从实验中验证了算法的有效性,并与遗传算法和粒子群算法比较,进一步证明了社会群体搜索算法在机器人路径规划问题中的有效性和高效性.展开更多
With the development of parallel computing technology,non-linear inversion calculation efficiency has been improving.However,for single-point search-based non-linear inversion methods,the implementation of parallel al...With the development of parallel computing technology,non-linear inversion calculation efficiency has been improving.However,for single-point search-based non-linear inversion methods,the implementation of parallel algorithms is a difficult issue.We introduce the idea of group search to the single-point search-based non-linear inversion algorithm, taking the quantum Monte Carlo method as an example for two-dimensional seismic wave velocity inversion and practical impedance inversion and test the calculation efficiency of using different node numbers.The results show the parallel algorithm in theoretical and practical data inversion is feasible and effective.The parallel algorithm has good versatility. The algorithm efficiency increases with increasing node numbers but the algorithm efficiency rate of increase gradually decreases as the node numbers increase.展开更多
文摘机器人学是现在及未来科技发展的重点,路径规划是机器人学中的一个重要课题.生物界一些群居动物有严格的等级制度和职责分工,受社会群居动物行为启发,提出社会群体搜索算法(social group search algorithm,SGSO).社会群体搜索算法对群体的分类及信息反馈机制——领导-追随机制的制定,降低了早熟的概率,交叉变异和淘汰机制的引入增加了搜索范围,减少了陷入局部最优的可能.同时,对提出的社会群体搜索算法进行了分析,从理论上证明了算法的收敛性;将社会群体搜索算法应用于机器人路径规划进行仿真,从实验中验证了算法的有效性,并与遗传算法和粒子群算法比较,进一步证明了社会群体搜索算法在机器人路径规划问题中的有效性和高效性.
基金supported by National Key S&T Special Projects of Marine Carbonate(No.2008ZX05000-004)CNPC Projects(No.2008E-0610-10)
文摘With the development of parallel computing technology,non-linear inversion calculation efficiency has been improving.However,for single-point search-based non-linear inversion methods,the implementation of parallel algorithms is a difficult issue.We introduce the idea of group search to the single-point search-based non-linear inversion algorithm, taking the quantum Monte Carlo method as an example for two-dimensional seismic wave velocity inversion and practical impedance inversion and test the calculation efficiency of using different node numbers.The results show the parallel algorithm in theoretical and practical data inversion is feasible and effective.The parallel algorithm has good versatility. The algorithm efficiency increases with increasing node numbers but the algorithm efficiency rate of increase gradually decreases as the node numbers increase.
