基于Baker映射的自适应樽海鞘群算法
投稿时间:2022-08-02  修订日期:2022-08-26  点此下载全文
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作者单位邮编
刘青 西安工程大学理学院 710048
贺兴时 西安工程大学理学院 710048
王耀军 宇航动力学国家重点实验室 
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目),
中文摘要:为了克服樽海鞘群算法(Salp Swarm Algorithm,SSA)求解准确性不足和易过早收敛的缺点,提出了一种多策略改进的樽海鞘群算法(MISSA)。引入Baker混沌映射生成樽海鞘群的初始种群,以提高初始个体的均匀性;将T分布策略应用到食物源位置公式中,对原始位置进行随机干扰,引导樽海鞘个体向最优解空间运动;在跟随者位置更新公式中引入不完全Γ函数的自适应权重,以改善算法的局部和全局搜索能力。将改进算法在 8 个测试函数上进行仿真实验,并与不同的群智能算法进行了比较。结果表明,改进算法具有更好的全局和局部搜索性能以及更高的搜索精度。
中文关键词:Baker映射初始化  T分布扰动策略  逆不完全Γ函数  樽海鞘群算法
 
An adaptive salp population algorithm based on Baker mapping
Abstract:In order to overcome the shortcomings of Salp Swarm Algorithm (SSA), a multi-strategy improved Salp Swarm Algorithm (MISSA) was proposed. Baker chaos mapping was introduced to generate the initial population of Salps to improve the uniformity of initial individuals. The t-distribution strategy was applied to the formula of food source location, and the original location was randomly disturbed to guide salps to the optimal solution space. The adaptive weight of incomplete γ function is introduced into the follower position update formula to coordinate and improve the local search and global exploration ability of the algorithm. The improved algorithm is simulated on eight test functions and compared with different swarm intelligence algorithms. The results show that the improved algorithm has better global and local search performance and higher search accuracy.
keywords:Baker mapping initialization  T distribution disturbance strategy  Inverse incomplete Γfunction  Salp Swarm Algorithm
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