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
针对室内到达时间差(time difference of arrival,TDOA)位置估计中的非线性最优化问题,提出用改进的樽海鞘群算法搜索目标位置.通过选择最优主基站构造改进的适应度函数,使适应度函数可以更好地反映解的优劣程度,提高了搜索精度.在初始...针对室内到达时间差(time difference of arrival,TDOA)位置估计中的非线性最优化问题,提出用改进的樽海鞘群算法搜索目标位置.通过选择最优主基站构造改进的适应度函数,使适应度函数可以更好地反映解的优劣程度,提高了搜索精度.在初始樽海鞘种群中引入近似解,使全局搜索的步骤得到简化,加快了算法前期收敛速度.采用自适应跟随策略更新追随者位置,解决局部开发低效问题,加快了算法后期收敛速度.仿真结果表明,基于改进樽海鞘群算法的TDOA定位技术相比其他元启发式算法具有更高的定位精度和更快的收敛速度.展开更多
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.
文摘针对室内到达时间差(time difference of arrival,TDOA)位置估计中的非线性最优化问题,提出用改进的樽海鞘群算法搜索目标位置.通过选择最优主基站构造改进的适应度函数,使适应度函数可以更好地反映解的优劣程度,提高了搜索精度.在初始樽海鞘种群中引入近似解,使全局搜索的步骤得到简化,加快了算法前期收敛速度.采用自适应跟随策略更新追随者位置,解决局部开发低效问题,加快了算法后期收敛速度.仿真结果表明,基于改进樽海鞘群算法的TDOA定位技术相比其他元启发式算法具有更高的定位精度和更快的收敛速度.
