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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Hybridizing InvasiveWeed Optimization with Firefly Algorithm for Multi-Robot Motion Planning
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Hybridizing InvasiveWeed Optimization with Firefly Algorithm for Multi-Robot Motion Planning

机译：杂草优化与Firefly算法混合，用于多机器人运动规划

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摘要

Path planning can be described as a NP-complete problem having various practical applications, as no polynomial time algorithm is capable of solving the problem available now. Autonomous mobile robot motion planning is one of these applications. In recent years, many researchers have developed algorithms inspired from the biological behaviour of different creatures. These techniques prove to be more efficient and robust. In this work, we have hybridized an evolutionary population-based algorithm invasive weed optimization technique which is derived from the invasive behaviour of weeds along with a swarm optimization algorithm, called firefly algorithm. Firefly optimization has been embedded into the existing invasive weed optimization to improve the position of the current weed population in the colony. The aim of hybridization is to avoid premature convergence in the large problem space and to maintain good balance between exploration and exploitation. The proposed hybridization IWFO maintains the efficient balance between exploration and exploitation because of adopting spatial dispersion property of IWO algorithm and movement property of the firefly, to explore new feasible region and exploits the new population of the weed colony. We have applied this hybrid algorithm for the motion planning of multi-robot in static environment. Computer simulation results prove the hybrid algorithm outperforms the traditional IWO Algorithm and firefly algorithm and overcomes the deficiencies of the individual algorithms.
机译：路径规划可以描述为具有各种实际应用的NP完全问题，因为没有多项式时间算法能够解决现在可用的问题。自主移动机器人运动计划就是这些应用程序之一。近年来，许多研究人员开发了受不同生物的生物学行为启发的算法。这些技术被证明是更加有效和健壮的。在这项工作中，我们将基于进化种群算法的入侵杂草优化技术与杂草的入侵行为以及称为萤火虫算法的群优化算法进行了混合。萤火虫优化已被嵌入到现有的侵入性杂草优化中，以改善当前杂草种群在群落中的位置。杂交的目的是避免在较大的问题空间中过早收敛，并在勘探与开发之间保持良好的平衡。提出的杂交IWFO通过采用IWO算法的空间分散特性和萤火虫的运动特性，在探索与开发之间保持了有效的平衡，从而探索了新的可行区域并开发了新的杂草种群。我们将此混合算法应用于静态环境下的多机器人运动规划。计算机仿真结果表明，该混合算法优于传统的IWO算法和萤火虫算法，克服了单个算法的不足。

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