特种部队算法:一种新的元启发式算法  

Special forces algorithm:A new meta-heuristic algorithm

作　　者：潘科 张伟[1] 王亚刚[1] PAN Ke;ZHANG Weit;WANG Ya-gang(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《控制与决策》2022年第10期2497-2504,共8页Control and Decision

基　　金：国家自然科学基金项目(11502145,61703277,61074087)。

摘　　要：根据现实环境中特种部队在执行任务时的情形,提出一种新的受启发于人的行为和基于群体的优化算法—–特种部队算法(SFA).特种部队算法为了有效模拟特种部队的特征,将现实中的不同战术行为和群体策略引入寻优思路,设计独特的搜索模式.特种部队会根据具体场景和任务要求,分别进行3种不同的任务:大规模搜索、突击搜捕、抓捕解救,通过结合不同策略,同时在算法中加入一些独特的机制, SFA可以模拟真实的动态行为以符合优化需求, SFA与其他几种成熟的算法进行比较,在包含单峰函数、多峰函数和固定维函数的15组基准函数测试中,验证了SFA的性能.实验结果表明, SFA展现出极具潜力和竞争力的结果,且能够在较好地平衡探索能力和开发能力的基础上,获得良好的搜索性能和寻优精度.In this paper, a new meta-heuristic algorithm inspired by human behaviors and the population-based optimizer,called special forces algorithm(SFA) is proposed based on the situation of special forces performing tasks in the real environment. In order to effectively simulate the characteristics of special forces, the SFA introduces different tactical behaviors and group strategies in reality into the optimization ideas, and designs a unique search mode. Special forces perform three tasks according to specific scenarios and mission requirements:large-scale search and assault search,capture and rescue. By combining the different strategies, and adding some uniquely mechanisms to the algorithm, the SFA simulates real dynamic behaviors to meet optimization requirements. The proposed SFA is compared with other types of mature algorithms, the performance of the SFA is verified in 15 sets of benchmark function tests including the unimodal function, multimodal function and fixed-dimensional function. The results demonstrate that the SFA has shown great potential and competitive results. The SFA can obtain good search performance and optimization accuracy on the basis of a better balance of exploration and exploitation capabilities.

关 键 词：元启发式 最优化 群智能 特种部队算法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]