Solid rocket propellants based on hydroxyl-terminated polybutadiene (HTPB), in which HTPB acts as the polymeric binder and fuel matrix, are widely used in aerospace propulsion. During storage, transport, and service, these composite energetic materials are exposed to sustained mechanical loads as well as environmental variations, which may induce time-dependent inelastic deformation. Such creep deformation can alter the grain geometry, affect combustion stability, and reduce the structural reliability of rocket motors. In this work, room-temperature tensile creep tests were conducted on an HTPB-based solid propellant under different stress levels. Several viscoelastic and power-law constitutive models were compared, and a composite time-hardening creep model was established to describe the experimental strain–time response. The model was further implemented in Abaqus through a Fortran user subroutine for finite element simulation. The results provide a useful basis for creep deformation assessment, formulation optimization, and structural reliability analysis of HTPB-based propellants.

The Horned Lizard Optimization Algorithm (HLOA) is a newly proposed swarm intelligence optimizer mimicking the defensive and survival behaviors of horned lizards. The original HLOA suffers evident drawbacks when tackling high-dimensional, multimodal and heavily constrained complicated optimization problems. Rapid decline in population diversity in late iterations and insufficient local optimum escape strategies further trigger premature convergence and unsatisfactory optimization precision. To systematically address the above deficiencies and boost the global optimization and engineering applicability of HLOA, this paper proposes a hybrid-strategy improved Horned Lizard Optimization Algorithm (HSHLOA). First, an improved uniform Logistic chaotic mapping replaces conventional random initialization. It enhances the ergodicity and uniformity of initial populations across search spaces and upgrades the quality of initial solutions and population diversity. Second, an adaptive optimal guidance strategy is constructed via nonlinear dynamic adjustment factors. It prioritizes global exploration in early iterations and strengthens local exploitation in later iterations to accelerate convergence and raise optimization accuracy. Third, a lens imaging learning strategy is embedded. It generates adaptive opposite solutions following dynamic convex lens optical imaging rules, strengthens the capability to escape local optima and mitigates premature convergence. To verify the optimization performance of the proposed algorithm, comparative experiments are conducted on the CEC 2017 benchmark test suite under 30-dimensional and 100-dimensional high-dimensional settings. Seven mainstream swarm intelligence algorithms are selected for benchmark comparison. Quantitative analyses cover convergence rate, optimization precision, numerical stability and local optimum escaping ability. Experimental results reveal that HSHLOA outperforms all peer competitors on unimodal, multimodal, hybrid and composite functions with remarkable superiority. The proposed HSHLOA is further applied to three typical constrained engineering optimization cases, including reinforced concrete beam design, three-bar truss design and pressure vessel design. Application results prove that HSHLOA satisfies all engineering constraints steadily and obtains superior structural schemes with higher efficiency. The reliability and superiority of HSHLOA for practical engineering problems are therefore verified.

The Ivy Optimization Algorithm (IVY) is a novel swarm intelligence optimization algorithm that simulates the phototropic growth mechanism of plants. To comprehensively improve the overall optimization performance, this paper proposes an enhanced Ivy Optimization Algorithm (LSIVY) integrating improved Logistics chaotic mapping, sine–cosine operator, and adaptive t-distribution mutation strategy. Firstly, an improved cascaded Logistics chaotic mapping is used for population initialization. The double arcsine transformation improves the ergodicity and uniformity of chaotic sequences, so that initial solutions are distributed more evenly in the search space, population diversity is enhanced, and premature convergence is suppressed. Secondly, the sine–cosine operator is embedded into the position update mechanisms of IVY growth, climbing, and propagation evolution. Nonlinearly decreasing control parameters realize adaptive switching between global exploration and local exploitation and accelerate convergence. Thirdly, an adaptive t-distribution mutation strategy is designed to dynamically adjust mutation intensity according to the iteration cycle and implement directional perturbation at the optimal solution position. It combines the large-scale exploration advantage of the Cauchy distribution and the local fine search merit of the Gaussian distribution, which significantly improves the ability to escape from local optima. Comparative experiments with eight mainstream metaheuristics (DE, WOA, GWO, HHO, DBO, MBWO, AOO, native IVY) are conducted with 30 independent runs on 30-dimensional CEC 2014 (30 test functions) and CEC 2020 (10 composite functions). Quantitatively, LSIVY achieves 20~30 orders of magnitude higher optimization accuracy than standard IVY on unimodal functions, and its average standard deviation across all benchmarks drops by 4–6 orders of magnitude. LSIVY ranks first on all CEC 2020 composite functions, reducing over 30% of iterations compared with native IVY. Three classical constrained mechanical design problems (three-bar truss, cantilever beam, pressure vessel) are adopted for engineering verification. In the pressure vessel case, the average manufacturing cost of LSIVY is reduced by 9.2% against standard IVY, and the standard deviation of three engineering cases decreases by 2–3 orders on average, demonstrating remarkable robustness. The proposed algorithm not only improves the theoretical system of plant-inspired swarm intelligence algorithms but also has great application prospects in mechanical structure lightweight design, industrial equipment cost optimization, and other practical engineering fields.