特种部队算法:一种新的元启发式算法
作者:
作者单位:

上海理工大学

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中图分类号:

TP273

基金项目:

国家自然科学基金项目(面上项,重点项目,重大项目)


Special Forces Algorithm: A new meta-heuristic algorithm
Author:
Affiliation:

University of Shanghai for Science and Technology

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    本文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,把现实中的不同战术行为和群体策略引入到寻优思路中,设计了独特的搜索模式.一般地,特种部队会根据具体场景和任务要求,分别进行三种不同的任务:大规模搜索、突击搜捕、抓捕解救.通过结合上述三种任务中展现的不同策略,同时在算法中加入一些设计独特的参数和机制,SFA可以模拟真实的动态模式和行为以符合优化需求.SFA与其他几种成熟的算法进行了比较,在包含单峰函数、多峰函数和固定维函数的21组基准函数测试中,验证了SFA的性能.结果表明,SFA展现出了极具潜力和竞争力的结果,SFA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.

    Abstract:

    In this paper, a new meta-heuristic algorithm inspired by human behavior and population-based optimizer is proposed, which is called Special Forces Algorithm (SFA). SFA is based on the situation of special forces performing tasks in the real environment. In order to effectively simulate the characteristics of special forces, SFA introduces different tactical behaviors and group strategies in reality into the optimization ideas. It designed a unique search mode. Generally, special forces can perform three tasks according to specific scenarios and mission requirements: large-scale search and assault search, capture and rescue. By combining the different strategies, adding some uniquely designed parameters and mechanisms to the algorithm, SFA can simulate real dynamic patterns or behaviors to meet optimization requirements. The proposed SFA is compared with other types Mature algorithms, the performance of SFA is verified in 21 sets of benchmark function tests including unimodal function, multimodal function and fixed-dimensional function. The results demonstrate that SFA has shown great potential and competitive results. SFA can obtain good search performance and optimization accuracy on the basis of a better balance of exploration and exploitation capabilities.

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历史
  • 收稿日期:2021-03-26
  • 最后修改日期:2021-07-09
  • 录用日期:2021-07-19
  • 在线发布日期: 2021-08-01
  • 出版日期: