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[Other] ACO DL : 0: 基于蚁群算法的机器人的路径规划问题蚁群算法，一种与传统的数学规划原理截然不同的，模拟自然生态系统以求解复杂优化问题(如NPC(NP Complete)类问题，典型的有TSP(Traveling Saleman Problem)问题)的仿生优化算法，因其较强分布式计算机制、鲁棒性、易于与其他方法相结合等优点，使得蚁群算法具有较广泛应用领域，为那些最优化技术难以解决的组合优化问题提供了一类新的切实可行的解决方案。从最初的一维的静态优化问题扩展到多维的动态组合优化问题，包括车辆路径规划，工程设计，电力系统，图像处理，通讯系统，机器人系统，以及制造系统等领域。该文所研究的内容是其中之一——机器人的路径规划问题。机器人路径规划是机器人学的一个重要研究领域，引起了众多研究者的关注。栅格法模型是众多环境建模方法中的一类实时性很强的路径规划模型。该文引入蚁群算法的基本思想，接着在基本蚁群算法上提出改进策略，并通过经典的旅行商问题验证改进蚁群算法的正确性，然后在改进的蚁群算法的基础上使用栅格法的路径规划策略, 并编制相应程序进行验证。 -Ant colony optimization, a mathematical programming with the traditional principle distinct simulate natural ecosystems to solve complex optimization problems (such as the NPC (NP Complete) class of problems, typically a TSP (Traveling Saleman Problem) problem) bionic optimization algorithm , because of its strong distributed computer system, robustness, ease combined with other methods, etc., makes the ant colony algorithm has a wider application areas, for those most difficult to solve optimization combinatorial optimization problems provide a new class of practical solutions. From the initial one-dimensional static optimization problem extended to multi-dimensional dynamic combinatorial optimization problems, including vehicle path planning, engineering design, power systems, image processing, communication systems, robotic systems, and manufacturing systems. In this paper, the contents of the study is one of these- robot path planning issues.
Date : 2026-01-11 Size : 1.05mb User : lin
[Other] ACO-PID DL : 0: 除了蚁群算法，可用于PID参数优化的智能算法还有很多，比如遗传算法、模拟退火算法、粒子群算法、人工鱼群算法，等等。-In addition to the ant colony algorithm can be used to optimize the PID parameters, there are many intelligent algorithms, such as genetic algorithms, simulated annealing algorithm, particle swarm optimization, AFSA, and so on.
Date : 2026-01-11 Size : 1kb User : 昊轩
[Other] ACO-PID DL : 0: 除了蚁群算法，可用于PID参数优化的智能算法还有很多，比如遗传算法、模拟退火算法、粒子群算法、人工鱼群算法，等等。-In addition to the ant colony algorithm, can be used in the intelligent algorithm of PID parameter optimization and there are many, such as genetic algorithm, simulated annealing algorithm, particle swarm optimization (pso), artificial fish algorithm, and so on.
Date : 2026-01-11 Size : 1kb User : 昊轩
[Other] aco DL : 0: In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis, the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. From a broader perspective, ACO performs a model-based search and share some similarities with Estimation of Distribution Algorithm.
Date : 2026-01-11 Size : 47kb User : reyhooon
[Other] code DL : 0: 基于蚁群算法的 TSP 求解，分别采用蚁群算法和蚁群算法-粒子群混合算法进行优化求解，使用不同的交叉和变异适应度函数更新粒子，从而实现 TSP问题的优化求解，更加逼近实际问题。(Based on the TSP solution of ant colony algorithm, ant colony algorithm and hybrid algorithm of ant colony algorithm particle swarm optimization are used to solve the TSP, and different fitness functions of crossover and mutation are used to update the particles, so as to achieve the optimal solution of TSP, which is closer to the actual problem.)
Date : 2026-01-11 Size : 5kb User : Fantasy1017