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Generating optimal paths in dynamic environments using River Formation Dynamics algorithm

机译:使用河流形成动力学算法在动态环境中生成最佳路径

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The paper presents a comparison of four optimisation algorithms implemented for the purpose of finding the shortest path in static and dynamic environments with obstacles. Two classical graph algorithms the Dijkstra complete algorithm and A* heuristic algorithm - were compared with metaheuristic River Formation Dynamics swarm algorithm and its newly introduced modified version. Moreover, another swarm algorithm has been compared - the Ant Colony Optimization and its modification. Terms and conditions of the simulation are thoroughly explained, paying special attention to the new, modified River Formation Dynamics algorithm. The algorithms were used for the purpose of generating the shortest path in three different types of environments, each served as a static environment and as a dynamic environment with changing goal or changing obstacles. The results show that the proposed modified River Formation Dynamics algorithm is efficient in finding the shortest path, especially when compared to its original version. In cases where the path should be adjusted to changes in the environment, calculations carried out by the proposed algorithm are faster than the A*, Dijkstra, and Ant Colony Optimization algorithms. This advantage is even more evident the more complex and extensive the environment is. (C) 2017 Elsevier B.V. All rights reserved.
机译:本文对四种优化算法进行了比较,这些算法是为了在有障碍的静态和动态环境中寻找最短路径而设计的。比较了两种经典的图算法Dijkstra完全算法和A *启发式算法-与超启发式河床动态群算法及其新引入的修改版。此外,还比较了另一种算法-蚁群优化及其修改。充分解释了模拟的条款和条件,并特别注意了新的,经过修改的“河流形成动力学”算法。该算法用于在三种不同类型的环境中生成最短路径的目的,每种环境分别用作静态环境和动态环境,目标不断变化或障碍不断变化。结果表明,所提出的改进的“河流形成动力学”算法可以有效地找到最短路径,特别是与原始版本相比。在需要根据环境变化调整路径的情况下,所提出的算法所进行的计算比A *,Dijkstra和蚁群优化算法要快。环境越复杂越广泛,这一优势就越明显。 (C)2017 Elsevier B.V.保留所有权利。

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