College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China.
Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China.
Sensors (Basel). 2019 Sep 23;19(19):4112. doi: 10.3390/s19194112.
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.
最近，研究人员和学者越来越关注开发元启发式算法，以解决许多研究领域的优化问题。本文提出了一种新颖的多群组拟仿射变换进化算法用于全局优化。通过随机将种群分为三组，增强了适应多群组拟仿射变换进化算法的种群多样性。每组采用不同的突变策略，以提高算法的效率。在搜索过程中，根据不同的策略自适应更新突变的比例因子 F，并适当调整参数，以在探索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。将实验结果与三种拟仿射变换进化算法变体、两种不同的进化变体以及两种粒子群优化变体进行比较，结果表明所提出的适应多群组拟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的适应多群组拟仿射变换进化算法的分析结果表明，所提出的方法比其他竞争算法产生更高的定位精度。
