An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots
An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots
复制标题
一种改进的协同进化遗传算法用于多移动机器人全局路径规划
DOI：
10.1016/j.neucom.2013.04.020
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发表时间：
2013-11-23
期刊：
影响因子：
6
通讯作者：
Alexander, Takacs
中科院分区：
文献类型：
--
作者：
Qu, Hong;Xing, Ke;Alexander, Takacs
针对多移动的机器人全局路径规划问题，提出了一种协同进化的改进遗传算法（CIGA）。该算法提出了一种有效、准确的适应度函数，对传统遗传算法的遗传算子进行了改进，提出了一种新的遗传修改算子。与传统遗传算法相比，改进后的遗传算法能更好地避免陷入局部最优，并具有更快的收敛速度。该算法充分考虑了种群间的合作，避免了移动的机器人之间的碰撞，有利于每个移动的机器人获得最优或近优的无碰撞路径。皇冠版权所有（c）2013由爱思唯尔B.V.出版保留所有权利。
This paper presents a Co-evolutionary Improved Genetic Algorithm (CIGA) for global path planning of multiple mobile robots, which employs a co-evolution mechanism together with an improved genetic algorithm (GA). This improved GA presents an effective and accurate fitness function, improves genetic operators of conventional genetic algorithms and proposes a new genetic modification operator. Moreover, the improved GA, compared with conventional GAs, is better at avoiding the problem of local optimum and has an accelerated convergence rate. The use of a co-evolution mechanism takes into full account the cooperation between populations, which avoids collision between mobile robots and is conductive for each mobile robot to obtain an optimal or near-optimal collision-free path. Simulations are carried out to demonstrate the efficiency of the improved GA and the effectiveness of CIGA. Crown Copyright (c) 2013 Published by Elsevier B.V. All rights reserved.