针对樽海鞘群算法在求解复杂优化问题时存在种群多样性减弱、易于陷入局部最优等不足,提出了一种使用高斯分布估计策略的改进樽海鞘群算法(salp swarm algorithm using elite pool strategy and Gaussian distribution estimation strat...针对樽海鞘群算法在求解复杂优化问题时存在种群多样性减弱、易于陷入局部最优等不足,提出了一种使用高斯分布估计策略的改进樽海鞘群算法(salp swarm algorithm using elite pool strategy and Gaussian distribution estimation strategy,GDESSA)。首先提出一种精英池选择策略,领导者位置在每次更新时随机从精英池中选择一个个体作为食物源,增强领导者的探索能力,丰富种群多样性。其次利用高斯分布估计策略对追随者公式进行改进,通过拟合优势群体信息,修正种群进化方向,增强算法的寻优能力。使用CEC2017测试函数对改进算法进行测试,并通过统计分析、收敛性分析、稳定性分析、Wilcoxon检验、Friedman检验、Iman-Davenport检验评估改进算法性能。仿真结果表明:本文提出的改进策略能有效提高算法性能;提出的改进算法相比其他算法,具有更快的收敛速度和更好的收敛精度。展开更多
In response to the shortcomings of the Salp Swarm Algorithm (SSA) such as low convergence accuracy and slow convergence speed, a Multi-Strategy-Driven Salp Swarm Algorithm (MSD-SSA) was proposed. First, food sources o...In response to the shortcomings of the Salp Swarm Algorithm (SSA) such as low convergence accuracy and slow convergence speed, a Multi-Strategy-Driven Salp Swarm Algorithm (MSD-SSA) was proposed. First, food sources or random leaders were associated with the current bottle sea squirt at the beginning of the iteration, to which Levy flight random walk and crossover operators with small probability were added to improve the global search and ability to jump out of local optimum. Secondly, the position mean of the leader was used to establish a link with the followers, which effectively avoided the blind following of the followers and greatly improved the convergence speed of the algorithm. Finally, Brownian motion stochastic steps were introduced to improve the convergence accuracy of populations near food sources. The improved method switched under changes in the adaptive parameters, balancing the exploration and development of SSA. In the simulation experiments, the performance of the algorithm was examined using SSA and MSD-SSA on the commonly used CEC benchmark test functions and CEC2017-constrained optimization problems, and the effectiveness of MSD-SSA was verified by solving three real engineering problems. The results showed that MSD-SSA improved the convergence speed and convergence accuracy of the algorithm, and achieved good results in practical engineering problems.展开更多
文摘针对樽海鞘群算法在求解复杂优化问题时存在种群多样性减弱、易于陷入局部最优等不足,提出了一种使用高斯分布估计策略的改进樽海鞘群算法(salp swarm algorithm using elite pool strategy and Gaussian distribution estimation strategy,GDESSA)。首先提出一种精英池选择策略,领导者位置在每次更新时随机从精英池中选择一个个体作为食物源,增强领导者的探索能力,丰富种群多样性。其次利用高斯分布估计策略对追随者公式进行改进,通过拟合优势群体信息,修正种群进化方向,增强算法的寻优能力。使用CEC2017测试函数对改进算法进行测试,并通过统计分析、收敛性分析、稳定性分析、Wilcoxon检验、Friedman检验、Iman-Davenport检验评估改进算法性能。仿真结果表明:本文提出的改进策略能有效提高算法性能;提出的改进算法相比其他算法,具有更快的收敛速度和更好的收敛精度。
文摘In response to the shortcomings of the Salp Swarm Algorithm (SSA) such as low convergence accuracy and slow convergence speed, a Multi-Strategy-Driven Salp Swarm Algorithm (MSD-SSA) was proposed. First, food sources or random leaders were associated with the current bottle sea squirt at the beginning of the iteration, to which Levy flight random walk and crossover operators with small probability were added to improve the global search and ability to jump out of local optimum. Secondly, the position mean of the leader was used to establish a link with the followers, which effectively avoided the blind following of the followers and greatly improved the convergence speed of the algorithm. Finally, Brownian motion stochastic steps were introduced to improve the convergence accuracy of populations near food sources. The improved method switched under changes in the adaptive parameters, balancing the exploration and development of SSA. In the simulation experiments, the performance of the algorithm was examined using SSA and MSD-SSA on the commonly used CEC benchmark test functions and CEC2017-constrained optimization problems, and the effectiveness of MSD-SSA was verified by solving three real engineering problems. The results showed that MSD-SSA improved the convergence speed and convergence accuracy of the algorithm, and achieved good results in practical engineering problems.
