针对樽海鞘算法(SSA)在寻优过程中存在收敛速度慢、易陷入局部最优等不足,本文提出了一种改进的樽海鞘算法,改进的樽海鞘算法采用正余弦搜索策略对领导者位置更新,以增强算法的全局搜索能力;同时,追随者引入差分策略对位置更新,以改善...针对樽海鞘算法(SSA)在寻优过程中存在收敛速度慢、易陷入局部最优等不足,本文提出了一种改进的樽海鞘算法,改进的樽海鞘算法采用正余弦搜索策略对领导者位置更新,以增强算法的全局搜索能力;同时,追随者引入差分策略对位置更新,以改善算法的局部搜索能力;食物源采用高斯变异,避免算法陷入局部最优。最后,将其用于三杆桁架优化设计问题中,通过数值实验,与传统粒子群算法、樽海鞘算法相比,改进的樽海鞘算法在三杆桁架模型求解中不会陷入局部最优且收敛速度更快。Aiming at the shortcomings of the standard salp swarm algorithm (SSA) in the process of optimization, such as slow convergence speed, low precision and insufficient convergence stability, which lead to local optimal and energy loss, this paper proposes an improved salp swarm algorithm. The improved salp swarm algorithm combines sine-cosine search strategy in the leading position, and enhances the global search and local development ability of the algorithm. At the same time, the followers introduce differential strategy to update the location and improve the local search ability of the algorithm. Food source adopts Gaussian variation to reduce the probability of the algorithm falling into the local optimal solution. Finally, the algorithm is applied to the optimization design of triangular truss, and the effectiveness of the improved salp swarm algorithm in solving triangular truss model is verified by numerical experiments compared with the traditional particle swarm optimization algorithm and salp swarm algorithm.展开更多
文摘针对樽海鞘算法(SSA)在寻优过程中存在收敛速度慢、易陷入局部最优等不足,本文提出了一种改进的樽海鞘算法,改进的樽海鞘算法采用正余弦搜索策略对领导者位置更新,以增强算法的全局搜索能力;同时,追随者引入差分策略对位置更新,以改善算法的局部搜索能力;食物源采用高斯变异,避免算法陷入局部最优。最后,将其用于三杆桁架优化设计问题中,通过数值实验,与传统粒子群算法、樽海鞘算法相比,改进的樽海鞘算法在三杆桁架模型求解中不会陷入局部最优且收敛速度更快。Aiming at the shortcomings of the standard salp swarm algorithm (SSA) in the process of optimization, such as slow convergence speed, low precision and insufficient convergence stability, which lead to local optimal and energy loss, this paper proposes an improved salp swarm algorithm. The improved salp swarm algorithm combines sine-cosine search strategy in the leading position, and enhances the global search and local development ability of the algorithm. At the same time, the followers introduce differential strategy to update the location and improve the local search ability of the algorithm. Food source adopts Gaussian variation to reduce the probability of the algorithm falling into the local optimal solution. Finally, the algorithm is applied to the optimization design of triangular truss, and the effectiveness of the improved salp swarm algorithm in solving triangular truss model is verified by numerical experiments compared with the traditional particle swarm optimization algorithm and salp swarm algorithm.
