摘要
为更好解决工程优化设计问题,改善樽海鞘群算法的寻优性能,提出一种引入有效缩放和随机交叉策略的自适应动态角色樽海鞘群算法。在领导者位置更新公式中引入帕累托分布和混沌映射,更有效地进行全局搜索;在全局和局部搜索的选择上,引入领导者—跟随者自适应调整策略,提高收敛精度;在局部搜索中引入随机交叉策略,增加种群多样性。将改进算法应用于不同典型复杂程度的工程优化问题中,测试结果表明:其寻优结果、问题适应性和求解稳定性优于其他算法。
In order to better solve the engineering optimization design problem and improve the optimization performance of salp swarm algorithm, an adaptive dynamic role salp swarm algorithm with effective scaling and random crossover strategy is proposed. A pareto distribution and chaotic map are introduced into the leader position updating formula to make global search more efficient. In the selection of global and local search, a leader-follower adaptive adjustment strategy is introduced to improve the convergence accuracy. A random crossover strategy is introduced in local search to increase population diversity. The improved algorithm is applied to engineering optimization problems with different typical complexity. The test results show that its optimization results, problem adaptability and solution stability are better than other algorithms.
作者
刘景森
袁蒙蒙
李煜
Liu Jingsen;Yuan Mengmeng;Li Yu(College of Software,Henan University,Kaifeng 475004,China;Institute of Intelligent Network System,Henan University,Kaifeng 475004,China;Institute of Management Science and Engineering,Henan University,Kaifeng 475004,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2021年第4期854-866,共13页
Journal of System Simulation
基金
河南省重点研发与推广专项(182102310886)
河南大学研究生教育创新与质量提升项目(SYL18060145,SYL19050104)。
关键词
樽海鞘群算法
帕累托分布函数
混沌映射
随机交叉策略
自适应调整策略
工程优化设计
salp swarm algorithm
pareto distribution function
chaotic maps
random crossover strategy
adaptive adjustment strategy
engineering optimization design