本网讯（通讯员：郭佳）近日，湖北经济学院信息工程学院大四学生肖海洋以第一作者身份在中科院三区期刊IEEE Access 上发表了了题为 “A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions“的论文。该论文聚焦于复杂单目标优化问题，提出了一种新的粒子群优化策略，对传统粒子群算法的效率提升具有重要意义，该论文指导老师为我院人工智能PI团队青年教师郭佳。
Abstract
Been trapped by local minimums is an important problem in no-linear optimization problems, which is blocking evolutionary algorithms to find the global optimum. Normally, to increase the optimization accuracy,evolutionary algorithms implement search around the best individual. However, overuse of information from a single individual can lead to a rapid diversity losing of the population, and thus reduce the search ability. To overcome this problem, a twinning memory bare-bones particle swarm optimization (TMBPSO) algorithm is presented in this work. The TMBPSO contains a twining memory storage mechanism (TMSM) and a multiple memory retrieval strategy (MMRS). The TMSM enables an extra storage space to extend the search ability of the particle swarm and the MMRS enhances the local minimum escaping ability of the particle swarm. The particle swarm is endowed with the ability of self-rectification by the cooperation of the TMSM and the MMRS. To verify the search ability of the TMBPSO, the CEC2017 benchmark functions and five state-of-the-art population-based optimization algorithms are selected in experiments. Finally, experimental results confirmed that the TMBPSO can obtain high accurate results for no-linear functions.
论文内容详细内容链接：https://ieeexplore.ieee.org/document/9953089