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An Adaptation Multi-Group Quasi-Affine Transformation Evolutionary Algorithm for Global Optimization and Its Application in Node Localization in Wireless Sensor Networks

机译：全局优化的自适应多组拟仿射变换进化算法及其在无线传感器网络节点定位中的应用

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摘要

Developing metaheuristic algorithms has been paid more recent attention from researchers and scholars to address the optimization problems in many fields of studies. This paper proposes a novel adaptation of the multi-group quasi-affine transformation evolutionary algorithm for global optimization. Enhanced population diversity for adaptation multi-group quasi-affine transformation evolutionary algorithm is implemented by randomly dividing its population into three groups. Each group adopts a mutation strategy differently for improving the efficiency of the algorithm. The scale factor F of mutations is updated adaptively during the search process with the different policies along with proper parameter to make a better trade-off between exploration and exploitation capability. In the experimental section, the CEC2013 test suite and the node localization in wireless sensor networks were used to verify the performance of the proposed algorithm. The experimental results are compared results with three quasi-affine transformation evolutionary algorithm variants, two different evolution variants, and two particle swarm optimization variants show that the proposed adaptation multi-group quasi-affine transformation evolutionary algorithm outperforms the competition algorithms. Moreover, analyzed results of the applied adaptation multi-group quasi-affine transformation evolutionary for node localization in wireless sensor networks showed that the proposed method produces higher localization accuracy than the other competing algorithms.
机译：研究人员和学者近来更加关注开发元启发式算法，以解决许多研究领域中的优化问题。本文提出了一种多组拟仿射变换进化算法的全局优化新方法。通过将种群随机分为三组来实现自适应多群拟仿射变换进化算法的增强种群多样性。每个小组采用不同的变异策略来提高算法的效率。在搜索过程中，使用不同的策略以及适当的参数来自适应地更新突变的比例因子F，以在勘探和开发能力之间取得更好的平衡。在实验部分，使用CEC2013测试套件和无线传感器网络中的节点定位来验证所提出算法的性能。将实验结果与三种准仿射变换进化算法变体，两种不同的进化变体和两种粒子群优化变体的结果进行比较，表明所提出的自适应多组准仿射变换进化算法优于竞争算法。此外，对无线传感器网络中的节点定位应用自适应多组拟仿射变换进化的分析结果表明，与其他竞争算法相比，该方法具有更高的定位精度。

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