Browsing by Author "Liu, Jinze"

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    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.
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    An adaptive trade-off evolutionary algorithm with composite differential evolution for constrained multi-objective optimization
    (Elsevier, 2023-08-19) Feng, Jian; Liu, Shaoning; Yang, Shengxiang; Zheng, Jun; Liu, Jinze
    Convergence, diversity, and feasibility are crucial factors in solving constrained multi-objective optimization problems (CMOPs). Their imbalance can result in the algorithm failing to converge well to the Pareto front, especially when dealing with complex CMOPs. To address this issue, we propose an adaptive tradeoff evolutionary algorithm (ATEA), which can adjust the environment selection strategy based on the characteristics of problem, aiming to achieve a balance between convergence and diversity while ensuring feasibility of the population. The ATEA divides the search process into three phases: In the extended exploration phase, a global search is conducted using a guided constraint relaxation technique to enable the population to quickly traverse the infeasible region and approach the feasible region. In the tradeoff exploration phase, constraints are further detected and estimated to retain more feasible individuals and competing infeasible individuals, allowing the population to accurately identify all possible feasible regions and gradually expand towards the feasible boundary. The exploitation phase explores under-explored regions in the earlier phases with the aim of accelerating the convergence of the population and escaping from the local optima. Extensive experiments conducted on four benchmark test suites demonstrate that ATEA exhibits superior performance in three benchmark test suites compared with six other state-of-the-art algorithms.
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    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.