基于对称映射搜索策略的自适应金鹰算法及应用

Adaptive Golden Eagle Algorithm Based on Symmetric Mapping Search Strategy and its Application

金鹰优化算法(Golden Eagle Optimizer,GEO)是一种基于种群的元启发式算法,其模拟了金鹰的合作狩猎行为。针对GEO算法中存在的求解精度差和陷入局部最优等问题,文中提出了一种改进MERGEO(Mapped Elitist Reverse GEO)算法。在原算法基础上采用对称映射搜索策略、自适应精英策略和随机反向学习机制这3种方法平衡了算法的探索和开发阶段,获得了规避局部最优能力和较好的优化精度。在10个基准测试函数上对该算法进行独立策略有效性分析、可扩展性分析以及同其他算法的优化性能比较分析。实验结果表明,改进后的MERGEO算法具有较强的竞争力和良好的优化能力。将改进后的算法用于无线传感器网络的覆盖优化问题和压力容器设计问题研究,验证了其实际应用价值。

The GEO(Golden Eagle Optimizer)is a population-based meta-heuristic algorithm that simulates the cooperative hunting behavior of golden eagles.In view of the problem of poor solution accuracy and local optima traps in the GEO algorithm,this study proposes an improved MERGEO(Mapped Elitist Reverse GEO)algorithm.Based on the original algorithm,symmetric mapping search strategy,adaptive elite strategy and random backward learning mechanism,are used to balance the exploration and development stages of the algorithm,and obtain the ability to avoid local optimal and better optimization accuracy.The independent strategy effectiveness analysis,scalability analysis and optimization performance comparison with other algorithms are carried out on 10 benchmark test functions.The experimental results show that the improved MERGEO algorithm has strong competitiveness and good optimization ability.The improved algorithm is applied to the coverage optimization problem of wireless sensor networks and pressure vessel design problem,which verifies the practical application value of improved algorithm.