摘要
针对海洋捕食者算法(Marine Predators Algorithm,MPA)收敛速度慢、求解精度低以及容易陷入局部最优等缺点,提出一种基于自适应旋转学习和危机意识策略的海洋捕食者算法(ARCMPA)。首先,针对MPA算法收敛速度慢、收敛精度低的问题,引入危机意识策略,提高算法探索解空间能力,加强算法前期开发能力不足,加快前期算法收敛速度,改善算法解的质量。其次,引入自适应旋转学习机制,使整个种群的位置分布更加均匀,有效增强算法在迭代时种群的多样性,避免算法在前期加快收敛速度后陷入局部最优。通过2种策略的引入,有效增强算法的整体性能。本文选取10个基准测试函数,并与其他元启发式算法进行比较。实验结果表明,以上改进有助于提高算法整体性能。
Aiming at the shortcomings of the Marine Predators Algorithm(MPA)such as slow convergence speed,low solution accuracy and easy to fall into local optimum,a Marine Predators Algorithm based on adaptive rotation learning and crisis aware⁃ness strategy(ARCMPA)is proposed.First of all,in view of the slow convergence speed and low convergence precision of the MPA,a crisis awareness strategy is introduced to improve the ability of the algorithm to explore the solution space,strengthen the lack of early development capabilities of the algorithm,speed up the convergence speed of the early algorithm,and improve the quality of the algorithm solution.Secondly,an adaptive rotation learning mechanism is introduced to make the position distri⁃bution of the entire population more uniform,effectively enhancing the diversity of the population during the iteration of the algo⁃rithm,and preventing the algorithm from falling into local optimum after accelerating the convergence speed in the early stage.Through the introduction of two strategies,the overall performance of the algorithm is effectively enhanced.In this paper,10 benchmark functions are selected and compared with other metaheuristic algorithms.Experimental results show that the above im⁃provements help to improve the overall performance of the algorithm.
作者
洪广杰
蔡茂国
詹楷杰
欧基发
HONG Guangjie;CAI Maoguo;ZHAN Kaijie;OU Jifa(College of Electronics and Information Engineering,Shenzhen University,Shenzhen 518001,China)
出处
《计算机与现代化》
2024年第7期112-119,共8页
Computer and Modernization
基金
广东省重点领域研发计划项目(2022B0101010002)。
关键词
元启发式
海洋捕食者
自适应旋转学习
危机意识策略
metaheuristics
marine predators
adaptive rotation learning
crisis awareness strategies
