Ant colony optimization algorithms are investigated in this paper for robot planning in configuration space.
本文研究了蚁群优化算法在机器人结构空间规划的问题。
The AI aficionados will find there possibilities to play with algorithms of Artificial Ant Colony Optimization and their parameters.
AI迷们会发现有可能与人工蚁群算法及其参数的算法玩。
To solve the multimodal optimization problem the 1d discrete optimization methods, the genetic and Ant Colony algorithms are applied.
为了解决多重模态最优化问题，我们运用了一维离散优化方法、遗传算法和蚁群算法。
Such algorithms include evolutionary algorithm (EA), particle swarm optimization (PSO), artificial immune system (AIS) and ant colony optimization (ACO) and so on.
这类算法主要包括进化算法(EA)、粒子群算法(PSO)、人工免疫系统(ais)和蚁群算法(aco)等等。
Ant colony algorithms are robust and adaptable as novel optimization methods.
蚁群算法作为一种新型的优化方法，具有很强的适应性和鲁棒性。
Ant colony algorithms are robust and adaptable as novel optimization methods.
蚁群算法作为一种新型的优化方法，具有很强的适应性和鲁棒性。
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