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- Ant Colony Optimization algorithm(ACO)has the limitations of poor convergence, and is easy to fall in local optima. 针对蚁群优化算法(Ant Colony Optimization; ACO)收敛速度慢、易陷于局部最优解的缺点;提出了一种基于协同进化思想的蚁群算法;用于求解TSP问题.
- A new ACO（Ant Colony Optimization） algorithm based on pheromone increment and diffusion models is presented. 文章提出一种新的基于信息素增量和扩散模型的蚁群算法。
- The concept of pheromone in Ant Colony Optimization have introduced to Evolutionary Algorithm for combinatorial optimization problems, a Gene Optimized Algorithm (GOA) is presented. 摘要从蚁群算法中得到启示，将信息素的观点引入到求解组合优化问题的演化算法之中，提出了一种基因优化算法。
- The fuzzy neural network controller based on ACO algorithm is proposed.The structure of fuzzy neural network controller is given, and its parameters are optimized by Ant Colony Optimization algorithm. 提出了一种基于蚁群优化算法的模糊神经网络控制系统，并介绍了模糊神经网络控制器的结构及其参数的蚁群优化算法。
- Aiming at the total weighted tardiness scheduling problem of unrelated parallel machine,a kind of hybrid ant colony optimization algorithm was proposed. 针对一类变速机总加权拖期调度问题,提出一种混合蚁群优化算法。
- Despite the broad application of GA(Genetic Algorithm) and ACO(Ant Colony Optimization algorithm) in multicast routing problem,they both have the paradox between the time and the optimization result. 遗传算法、蚁群优化算法已在多播路由优化问题中得到了广泛应用,但由于算法本身的缺陷,二者在具体应用时都存在着时间性能与优化性能之间的矛盾。
- It can improve the classical ant colony optimization algorithm(ACO)by combining adaptive dynamic routing algorithm.ACO has worse performance in convergence and is easy to fall into the local maximum. 蚁群算法有收敛性较差、易于过早陷入局部最优等不足,通过构建蚁群、引入信息素约束条件、调整信息素初始值、自适应改变信息素增量等技术,增强了蚁群搜索能力,改善了算法收敛速度。
- By adding gradient information to influence the update of the local search of the ants, a kind of ant colony optimization algorithm with gradient information is proposed. 针对第三章提出的ACOAL算法由于随机性搜索过强导致收敛时间过长的缺点;提出了带有梯度加速的蚁群混合算法(GACO).
- Ant Colony Optimization algorithm (ACO) 蚁群优化算法
- Ant Colony Optimization algorithm(ACO) 蚁群算法
- ant colony optimization algorithm 蚁群优化算法
- Improved Ant Colony Optimization Algorithm 改进蚁群优化算法
- Binary Ant Colony Optimization Algorithm 二元蚁群优化算法
- Continuoas ant colony optimization algorithm 连续蚁群算法
- ACO ( Ant colony optimization algorithm ) 蚁群算法
- ant colony optimization algorithm (ACOA) 蚁群优化算法
- ant colony optimization algorithm(ACA) 蚁群优化算法
- continuous ant colony optimization algorithm 连续蚁群算法
- chaos ant colony optimization algorithm 混沌蚁群算法
- Inspired by the collective behavior of real ants, Dorigo, Maniezzo et al first advanced an ant colony system and the ant colony optimization algorithm(ACO) to solve several discrete optimization problems. Dorigo等人受到自然界中真实蚁群集体行为的启发而首先提出来的。 大量实验结果表明,它在解决许多组合优化问题时都能表现出较好的求解能力,经过了许多国内外学者不断地对其进行扩展和改进,蚁群算法正经历着一个不断发展和完善的过程。
