Improving the Efficiency of Multi-Objective Grasshopper Optimization Algorithm to Enhance Ontology Alignment

Ontology alignment is an essential and complex task to integrate heterogeneous ontology.The meta-heuristic algorithm has proven to be an effective method for ontology alignment.However,it only applies the inherent advantages of metaheuristics algorithm and rarely considers the execution efficiency,especially the multi-objective ontology alignment model.The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications.In this paper,two multi-objective grasshopper optimization algorithms(MOGOA)are proposed to enhance ontology alignment.One isε-dominance concept based GOA(EMO-GOA)and the other is fast Non-dominated Sorting based GOA(NS-MOGOA).The performance of the two methods to align the ontology is evaluated by using the benchmark dataset.The results demonstrate that the proposed EMO-GOA and NSMOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods.

基于改进MOGOA的无人机群航迹规划研究

针对电子侦察系统中反辐射无人机群进行辐射源无源定位时机群的编队形式会对定位精度产生影响的问题,将克拉美-罗界(Cramer-Rao lower bound,CRLB)作为定位精度方面的优化目标,与其他优化目标、约束一起引入机群的航迹规划中,使无人机群运动过程中保持良好编队,确保无源定位精度.文中针对多优化目标复杂环境中航迹规划算法寻优能力不高的问题,提出了一种基于改进多目标蝗虫算法(IMOGOA)的无人机群3维航迹规划方法,通过对MOGOA的选择方式、收敛参数进行改进从而提高算法的收敛性能以及全局搜索性能.首先,建立无人机群航迹规划的运动学模型,并引入距离约束,除定位精度以外还引入了路程、威胁代价等作为航迹规划的优化目标函数,然后,对改进多目标蝗虫算法进行详细说明,最后设计基于IMOGOA的无人机群航迹规划方案的算法流程,并在设定场景中对该算法的性能进行了仿真分析.结果表明,所提出的IMOGOA能够成功地规划出无人机群从初始位置到辐射源位置处的3维航迹,同时使无人机群在运动过程中保持良好的定位精度,经IMOGOA规划的机群编队定位精度最高可达1.2%,性能明显优于正方形编队和随机编队,并通过将IMOGOA与原始蝗虫算法(GOA)、原始多目标蝗虫算法进行对比,结果表明IMOGOA的收敛速度比MOGOA快11.1%,搜索性能相较GOA提升13.8%.