Browsing by Author "Zhang, Huaguang"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemEmbargo
    A novel multi-level hierarchy optimization algorithm for inner detector speed control
    (Elsevier, 2025-02-13) Liu, Jinze; Feng, Jian; Zhang, Huaguang; Yang, Shengxiang
    This paper proposes a novel nature-inspired algorithm called Multi-Level Hierarchy Optimization (MLHO) for solving optimization problems over continuous space. The MLHO algorithm is inspired by the hierarchy of nature, especially the hierarchy of biological populations. The entire algorithm structure is divided into four levels for iterative optimization, and the work of each level is global direction guidance, optimization-seeking task allocation, local optimal exploration, and broad domain exploration. Differential variation strategy and dynamic inertia factor are also designed to solve the problem of decreasing population diversity and slow convergence speed at the late stage of evolution. In order to validate and analyze the performance of MLHO, numerical experiments were conducted on benchmark problems in each dimension of CEC'20. In addition, comparisons with 4 state-of-the-art (SOTA) algorithms are executed. The results show that the performance of MLHO is significantly superior to, or at least comparable to the SOTA algorithms. At the same time, the feasibility and effectiveness of MLHO are also demonstrated for the speed control problem of the pipeline inner detector.
  • Thumbnail Image
    ItemOpen Access
    Dynamic ε-multilevel hierarchy constraint optimization with adaptive boundary constraint handling technology
    (Elsevier, 2023-12-20) Liu, Jinze; Feng, Jian; Yang, Shengxiang; Zhang, Huaguang; Liu, Shaoning
    Real-world optimization problems are often difficult to solve because of the complexity of the objective function and the large number of constraints that accompany it. To solve such problems, we propose Adaptive Dynamic ε-Multilevel Hierarchy Constraint Optimization (εMHCO). Firstly, we propose the dynamic constraint tolerance factor ε which can change dynamically with the feasible ratio and the number of iterations in the current population. This ensures a reasonable proportion of virtual feasible solutions in the population. Secondly, we propose adaptive boundary constraint handling technology (ABCHT). It can reshape the current individual position adaptively according to the size of constraint violation and increase the diversity of the population. Finally, we propose multi-level hierarchy optimization, whose multiple population structure is beneficial to solve real-world constraint optimization problems (COPs). To validate and analyze the performance of εMHCO, numerical experiments are conducted on the latest real-world test suite CEC’2020, which contains a set of 57 real-world COPs, and compared with four state-of-the-art algorithms. The results show that εMHCO is significantly superior to, or at least comparable to the state-of-the-art algorithms in solving real-world COPs. Meanwhile, the effectiveness and feasibility of εMHCO are verified on the real-world problem of the pipeline inner detector speed control.