When resolving many-objective problems, multi-objective optimization algorithms encounter several difficulties degrading their performances. These difficulties may concern the exponential execution time, the effectiveness of the mutation and recombination operators or finding the tradeoff between diversity and convergence. In this paper, the issue of 3D redeploying in indoor the connected objects (or nodes) in the Internet of Things collection networks (formerly known as wireless sensor nodes) is investigated. The aim is to determine the ideal locations of the objects to be added to enhance an initial deployment while satisfying antagonist objectives and constraints. In this regard, a first proposed contribution aim to introduce an hybrid model that includes many-objective optimization algorithms relying on decomposition (MOEA/D, MOEA/DD) and reference points (Two_Arch2, NSGA-III) while using two strategies for introducing the preferences (PI-EMO-PC) and the dimensionality reduction (MVU-PCA). This hybridization aims to combine the algorithms advantages for resolving the many-objective issues. The second contribution concerns prototyping and deploying real connected objects which allows assessing the performance of the proposed hybrid scheme on a real world environment. The obtained experimental and numerical results show the efficiency of the suggested hybridization scheme against the original algorithms.

中文翻译：

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使用混合多目标优化算法的IoT网络3D部署

在解决多目标问题时，多目标优化算法会遇到一些降低性能的难题。这些困难可能涉及指数执行时间，突变和重组算子的有效性或寻找多样性和收敛性之间的折衷。本文研究了3D在室内重新部署物联网收集网络（以前称为无线传感器节点）中连接的对象（或节点）的问题。目的是确定要添加的对象的理想位置，以增强初始部署，同时满足反对者的目标和约束。在这方面，第一个提议的贡献旨在引入一种混合模型，该模型包括依赖于分解的多目标优化算法（MOEA / D，MOEA / DD）和参考点（Two_Arch2，NSGA-III），同时使用两种策略来引入偏好（PI-EMO-PC）和降维（MVU-PCA）。这种混合旨在结合算法优势来解决多目标问题。第二个贡献涉及原型设计和部署实际的连接对象，这允许评估所提出的混合方案在现实环境中的性能。获得的实验结果和数值结果表明了所提出的杂交方案相对于原始算法的效率。第二个贡献涉及原型设计和部署实际的连接对象，这可以评估所提出的混合方案在现实环境中的性能。获得的实验结果和数值结果表明了所提出的杂交方案相对于原始算法的效率。第二个贡献涉及原型设计和部署实际的连接对象，这允许评估所提出的混合方案在现实环境中的性能。获得的实验结果和数值结果表明了所提出的杂交方案相对于原始算法的效率。