Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

基于DGWO-SVM的气象环境下武器作战效能评估方法

实际环境中,各种气象环境要素对于武器装备的作战效能影响显著,对于武器装备的作战效能评估很困难。为提高在气象环境影响下的武器装备作战效能评估效率,提出一种基于强化首领决策灰狼优化-支持向量机(DGWO-SVM)算法的评估模型。建立一个支持向量机模型应用于作战效能评估中,结合强化首领决策能力的灰狼优化算法进行求解,对惩罚因子以及核函数参数进行优化选取,并将最终的评估结果与其他方法进行比对。实验结果表明,所提评估模型的准确率达到了98.39%,评估的误差结果更优于其他方法,具有更高的实用性和有效性。