论文标题
使用改良蝴蝶优化算法的机器人的未知区域勘探
Unknown area exploration for robots with energy constraints using a modified Butterfly Optimization Algorithm
论文作者
论文摘要
蝴蝶优化算法（BOA）是最近在几种优化问题中使用的元硫素化。在本文中，我们根据交叉操作员提出了一种新版本的算法（XBOA），并将其结果与最初的BOA和最近在文献中引入的其他3种变体进行了比较。我们还提出了一个框架，用于在单次和多机器人方案中使用元启发式学用能量限制来解决未知区域勘探问题。这个框架使我们能够为机器人探索问题进行不同的元启发式学的表现。我们进行了几项实验来验证该框架，并将其用来将XBOA的有效性与通过5个评估标准在文献中使用的众所周知的元硫疗法进行比较。尽管BOA和XBOA在所有这些标准上都不是最佳的，但我们发现，就探索时间而言，BOA可以替代许多元启发式学，而XBOA对本地Optima更为强大。具有更好的健身融合；比原始的BOA及其其他变体可以实现更好的勘探率。
Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving the unknown area exploration problem with energy constraints using metaheuristics in both single- and multi-robot scenarios. This framework allowed us to benchmark the performances of different metaheuristics for the robotics exploration problem. We conducted several experiments to validate this framework and used it to compare the effectiveness of xBOA with wellknown metaheuristics used in the literature through 5 evaluation criteria. Although BOA and xBOA are not optimal in all these criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima; has better fitness convergence; and achieves better exploration rates than the original BOA and its other variants.
