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。
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 incompleteΓfunction 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.
作者
刘青
贺兴时
王耀军
LIU Qing;HE Xing-shi;WANG Yao-jun(School of Science,Xi’an Polytechnic University,Xi’an,Shaanxi 710048,China;National Key Laboratory of Aerospace Dynamics,Xi’an,Shaanxi 710000,China)
出处
《计算技术与自动化》
2023年第3期72-78,共7页
Computing Technology and Automation
基金
国家自然科学基金资助项目(12101477)
陕西省自然科学基础研究计划(2020JQ-831)。
