Adaptive gaining-sharing knowledge-based variant algorithm with historical probability expansion and its application in escape maneuver decision making

To further improve the performance of adaptive gaining-sharing knowledge-based algorithm (AGSK), a novel adaptive gaining sharing knowledge-based algorithm with historical probability expansion (HPE-AGSK) is proposed by modifying the search strategies. Based on AGSK, three improvement strategies are proposed. First, expansion sharing strategy is proposed and added in junior gaining-sharing phase to boost local search ability. Second, historical probability expansion strategy is proposed and added in senior gaining-sharing phase to strengthen global search ability. Last, reverse gaining strategy is proposed and utilized to expand population distribution at the beginning of iterations. The performance of HPE-AGSK is initially evaluated using IEEE CEC 2021 test suite, compared with fifteen state-of-the-art algorithms (AGSK, APGSK, APGSK-IMODE, GLAGSK, EDA2, AAVS-EDA, EBOwithCMAR, LSHADE-SPACMA, HSES, IMODE, MadDE, CJADE, and iLSHADE-RSP). The results demonstrate that HPE-AGSK outperforms both state-of-the-art GSK-based variants and past winners of IEEE CEC competitions. Subsequently, GSK-based variants and other exceptional algorithms in CEC 2021 are selected to further evaluate the performance of HPE-AGSK using IEEE CEC 2018 test suite. The statistical results show that HPE-AGSK has superior exploration ability than the comparison algorithms, and has strong competition with APGSK (state-of-the-art AGSK variant) and IMODE (CEC 2020 Winner) in exploitation ability. Finally, HPE-AGSK is utilized to solve the beyond visual range escape maneuver decision making problem. Its success rate is 100%, and mean maneuver time is 9.10 s, these results show that HPE-AGSK has good BVR escape maneuver decision-making performance. In conclusion, HPE-AGSK is a highly promising AGSK variant that significantly enhances the performance, and is an outstanding development of AGSK. The code of HPE-AGSK can be downloaded from https://github.com/xieleilei0305/HPE-AGSK-CODE.git. (The link will be available for readers after the paper is published).