The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method...The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method for the path planning. In the paper the traditional ant colony algorithm is improved, and measures of keeping optimization, adaptively selecting and adaptively adjusting are applied, by which better path at higher convergence speed can be found. Finally the algorithm is implemented with computer simulation and preferable results are obtained.展开更多
Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D pa...Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.展开更多
In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwa...In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a nnmerical solution of a nonlinear optimal control problem （NOCP）. An energy performance index as a cost function, which should be minimized, was defmed. The resulting problem was a two-point boundary value problem （TPBVP）. A genetic algorithm （GA）, particle swarm optimization （PSO）, and ant colony optimization （ACO） algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.展开更多
结合蚁群算法(ant colony optimization,ACO)与遗传算法(genetic algorithm,GA)的传统路径规划方法普遍存在路径不平滑、收敛速度慢及能耗较高等问题。为解决上述问题,提出了一种动态融合ACO与GA(dynamic fusion of ACO and GA,DACO-GA...结合蚁群算法(ant colony optimization,ACO)与遗传算法(genetic algorithm,GA)的传统路径规划方法普遍存在路径不平滑、收敛速度慢及能耗较高等问题。为解决上述问题,提出了一种动态融合ACO与GA(dynamic fusion of ACO and GA,DACO-GA)的路径规划方法,以提升路径规划的效率与精度。该方法初期采用ACO生成初始种群,并引入GA进行优化调整;在后续阶段,通过动态切换2种算法的主导角色,实现全局与局部搜索的协调互补。算法设计中融合了自适应信息素分布、动态挥发因子及自适应交叉/变异概率调节机制,有效提升了搜索能力并缓解了局部最优问题。最后,围绕DACO-GA中的关键控制参数开展优化实验,以验证各改进机制的有效性。在多个典型场景下将DACO-GA与传统算法进行对比,以进一步评估其在复杂环境下的适应性。结果表明,所提出的算法可生成更平滑且长度更短的路径,展现出良好的全局优化能力以及较快的收敛速度。DACO-GA不仅为复杂路径规划问题提供了有效的解决方案,还可为多智能体协作、机器人导航等领域的优化提供技术参考。展开更多
文摘The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method for the path planning. In the paper the traditional ant colony algorithm is improved, and measures of keeping optimization, adaptively selecting and adaptively adjusting are applied, by which better path at higher convergence speed can be found. Finally the algorithm is implemented with computer simulation and preferable results are obtained.
基金Supported by National Natural Science Foundation of China (50875165)
文摘Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.
文摘In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a nnmerical solution of a nonlinear optimal control problem （NOCP）. An energy performance index as a cost function, which should be minimized, was defmed. The resulting problem was a two-point boundary value problem （TPBVP）. A genetic algorithm （GA）, particle swarm optimization （PSO）, and ant colony optimization （ACO） algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.
文摘结合蚁群算法(ant colony optimization,ACO)与遗传算法(genetic algorithm,GA)的传统路径规划方法普遍存在路径不平滑、收敛速度慢及能耗较高等问题。为解决上述问题,提出了一种动态融合ACO与GA(dynamic fusion of ACO and GA,DACO-GA)的路径规划方法,以提升路径规划的效率与精度。该方法初期采用ACO生成初始种群,并引入GA进行优化调整;在后续阶段,通过动态切换2种算法的主导角色,实现全局与局部搜索的协调互补。算法设计中融合了自适应信息素分布、动态挥发因子及自适应交叉/变异概率调节机制,有效提升了搜索能力并缓解了局部最优问题。最后,围绕DACO-GA中的关键控制参数开展优化实验,以验证各改进机制的有效性。在多个典型场景下将DACO-GA与传统算法进行对比,以进一步评估其在复杂环境下的适应性。结果表明,所提出的算法可生成更平滑且长度更短的路径,展现出良好的全局优化能力以及较快的收敛速度。DACO-GA不仅为复杂路径规划问题提供了有效的解决方案,还可为多智能体协作、机器人导航等领域的优化提供技术参考。
