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首页> 外文会议>IEEE International Conference on Computational Intelligence and Cybernetics;IEEE International Conference on Communication, Networks and Satellite >A metaheuristic optimization algorithm for unsupervised robotic learning
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A metaheuristic optimization algorithm for unsupervised robotic learning

机译：一种无监督机器人学习的成群质优化算法

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摘要

A meta-heuristic search algorithm intended to introduce chaotic dynamics and Levy flights into the algorithm is presented in this paper. Among most evolutionary computation for optimization problem including meta-heuristic search algorithms, the solution is drawn like a moth to a flame and cannot keep away. The fine balance between intensification (exploitation) and diversification (exploration) is very important to the overall efficiency and performance of an algorithm. Too little exploration and too much exploitation could cause the system to be trapped in local optima, which makes it very difficult or even impossible to find the global optimum. The track of chaotic variable can travel ergodically over the whole search space. In general, the chaotic variable has special characters, i.e., ergodicity, pseudo-randomness and irregularity. To enrich the searching behavior and to avoid being trapped into local optimum, chaotic sequence and a chaotic Levy flight are incorporated in the meta-heuristic search for efficiently generating new solutions. The proposed algorithm with quite general objective function is used to study the ability to develop unsupervised robotic learning such as the maze exploring ability.
机译：本文提出了一种旨在将混沌动力学和征集航班引入算法的元启发式搜索算法。在包括元启发式搜索算法的优化问题的大多数进化计算中，解决方案就像一个飞蛾一样绘制到火焰并且不能远离。强化（剥削）和多样化（勘探）之间的细平衡对算法的整体效率和性能非常重要。太少的探索和太多的开采可能导致系统被困在当地的最佳状态，这使得这使得非常困难或甚至不可能找到全球最佳。混沌变量的轨道可以在整个搜索空间上令人讨厌地行进。通常，混沌变量具有特殊字符，即遍历，伪随机性和不规则性。为了丰富搜索行为并避免被困成局部最佳，混沌序列和混乱的征集飞行，并在元启发式搜索中被纳入有效地产生新的解决方案。具有相当一般的目标函数的提议算法用于研究制定无监督机器人学习的能力，例如迷宫探索能力。

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