樽海鞘群算法的改进  

Improvements of slap swarm algorithm

作　　者：常祥洁 赵孜恺 周朝荣[1,3] CHANG Xiang-jie;ZHAO Zi-kai;ZHOU Zhao-rong(School of Physics and Electronic Engineering,Sichuan Normal University,Chengdu 610101,China;International Business Division,Accelink Technologies Limited Company,Wuhan 430205,China;Meteorological Information and Signal Processing Key Laboratory of Sichuan Higher Education Institutes,Chengdu University of Information Technology,Chengdu 610225,China)

机构地区：[1]四川师范大学物理与电子工程学院,四川成都610101 [2]武汉光迅科技股份有限公司国际营销部,湖北武汉430205 [3]成都信息工程大学气象信息与信号处理四川省高校重点实验室,四川成都610225

出　　处：《计算机工程与设计》2022年第7期1941-1948,共8页Computer Engineering and Design

基　　金：气象信息与信号处理四川省高校重点实验室开放课题基金项目(QXXCSYS201704);四川省教育厅重点基金项目(15CZ0004);四川省高等教育人才培养质量和教学改革基金项目(JG2018-683)。

摘　　要：针对樽海鞘群算法在求解过程中存在收敛速度慢、寻优精度低等问题,提出改进的樽海鞘群算法。采用混沌初始化,保证种群的多样性和均匀性;分别在领导者和追随者阶段引入正弦余弦策略和动态更新策略,提高算法全局和局部探索能力;对食物位置进行变异操作,有效避免算法陷入局部最优。为验证改进后算法的有效性,分别用其求解函数优化问题以及工程设计问题,其结果表明,该算法具有较高的收敛速度、寻优精度以及鲁棒性,总体性能优于其它智能优化算法。Due to the slow convergence and low search accuracy during the problem-solving process,the improved salp swarm algorithm(ISSA)was proposed.The chaotic initialization was employed to ensure the diversity and uniformity of populations.The sine cosine strategy and dynamic updating strategy were introduced in the leader and follower stages respectively to improve the global and local search ability of the algorithm.The mutation operation was used for food positions to effectively avoid the algorithm falling into local optimum solutions.To verify the effectiveness of the improved algorithm,it was used to solve the function optimization and engineering design problems.The results demonstrate that the improved algorithm has higher convergence speed,search accuracy and robustness,and its overall performance is better than that of other intelligent optimization algorithms.

关 键 词：智能优化算法 樽海鞘群算法 混沌初始化 正弦余弦策略 动态更新 函数优化问题 工程设计问题 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]