基于布谷鸟搜索算法的多平台协同搜索任务规划方法研究  

Research into Multi-Platform Collaborative Search Mission Planning Method Based on Cuckoo Search Algorithm

作　　者：白爽 姜宁[1] 秦浩 BAI Shuang;JIANG Ning;QIN Hao(Dalian Naval Academy,Dalian 116000,China)

机构地区：[1]海军大连舰艇学院,辽宁大连116000

出　　处：《舰船电子对抗》2025年第2期71-75,79,共6页Shipboard Electronic Countermeasure

摘　　要：针对多平台协同搜索任务分配规划问题,设计并实现了一种基于布谷鸟搜索算法的优化方法。在复杂任务场景下,该方法能够为每架平台分配搜索任务,使其在续航时间内最大化总搜索收益,并在任务完成后返回最近的基地。提出利用帐篷映射和高斯分布优化布谷鸟搜索算法的方法,设计适应度函数对搜索任务分配方案进行评估,并通过实验设计和数据分析对提出的方法进行了验证。实验结果表明,该方法在总搜索收益、完成任务数量和平均收敛时间上均优于传统布谷鸟算法,能更高效地完成任务分配,算法在不同解空间中表现出一定的多样性,能够在多种分配策略下达到相同的收益目标,体现了其稳定性和有效性。Aiming at the task assignment planning problem of multi-platform collaborative search,an optimization method based on cuckoo search algorithm is designed and implemented.In complex mission scenarios,the method can allocate search tasks to each platform,so that they maximize the total search payoff within their endurance time and return to the nearest base after mission completion.The paper proposes optimizing the cuckoo search algorithm by using tent mapping and Gaussian distribution,designs a fitness function to evaluate the search task allocation plans,and validates the proposed method through experiment design and data analysis.Experimental results show that the proposed method outperforms the traditional cuckoo search algorithm in terms of total search payoff,number of tasks completed and average convergence time,and can more efficiently complete task allocation.The algorithm demonstrates a certain degree of diversity in different solution spaces and can achieve the same payoff purpose under various allocation strategies,which reflects its stability and effectiveness.

关 键 词：任务规划 协同搜索 优化算法 布谷鸟搜索算法 

分 类 号：TN971.1[电子电信—信号与信息处理]