Wild Horse Optimizer (WHO), a new metaheuristic algorithm proposed in recent years, has some weaknesses in solving practical problems, such as low searching accuracy and slow convergence speed. Herein, a Hybrid Multi‐Strategy improved Wild Horse Optimizer (HMSWHO) is proposed, which includes four strategies to improve the optimization capability. The Halton sequence is used to initialize the foal population to make the population more diverse. The adaptive parameter TDR is improved to balance the global exploration and local exploitation. The simplex method is used to improve the worst position of the population. Wild horse escaping behavior is added to improve search efficiency and optimization accuracy. The main innovation strategies are the improvement of TDR and the addition of escaping behavior. To verify the effectiveness of the improved strategies, 12 benchmark test functions, CEC2017, and CEC2021 test functions are selected for simulation experiments. Mechanical design examples are added for optimization, and the optimization results are 16.61%, 1.65%, and 0.21% less than that of WHO. The results show that the improved algorithm has obvious advantages in convergence speed, accuracy, and stability. HMSWHO can be applied to more practical engineering optimization problems and provide new ideas for structural optimization methods.

Aiming at the shortcomings of seagull optimization algorithm in the process of searching for optimization, such as slow convergence speed, low precision, easy falling into local optimal, and performance dependent on the selection of parameters, this paper proposes an improved gull optimization algorithm based on multi-strategy fusion based on the analysis of gull population characteristics. Firstly, L–C cascade chaotic mapping is used to initialize the population so that seagulls are more evenly distributed in the initial solution space. Secondly, to improve the algorithm’s global exploration ability in the early stage, the nonlinear convergence factor is incorporated to adjust the position of seagulls in the migration stage. At the same time, the group learning strategy was introduced after the population position update to improve the population quality and optimization accuracy further. Finally, in the late stage of the algorithm, the golden sine strategy of the Levy flight guidance mechanism is used to update the population position to improve the population’s diversity and enhance the local development ability of the algorithm in the late stage. To verify the optimization performance of the improved algorithm, CEC2017 and CEC2022 test suites are selected for simulation experiments, and box graphs are drawn. The test results show that the proposed algorithm has apparent convergence speed, accuracy, and stability advantages. The engineering case results demonstrate the proposed algorithm’s advantages in solving complex problems with unknown search spaces.

The slime mould algorithm (SMA), a revolutionary metaheuristic algorithm with streamlined operations and processes, is frequently utilized to solve optimization issues in various fields. This paper proposed a modified slime mold method (HKTSMA) based on multiple adaptive strategies to ameliorate the convergence speed and capacity to escape local optima. In HKTSMA, the scrambled Halton sequence was utilized to increase population uniformity. By Adjusting the oscillation factor, HKTSMA performs better in controlling the step length and convergence. A novel learning mechanism was proposed based on the k-nearest neighbor clustering method that significantly improved the convergence speed, accuracy, and stability. Then, to increase the probability of escaping the local optima, an enhanced adaptive t-distribution mutation strategy was applied. Simulation experiments were conducted with 32 test functions chosen from 23 commonly used benchmark functions, CEC2019 and CEC2021 test suite and 3 real-world optimization problems. The results demonstrated the effectiveness of each strategy, the superior optimization performance among different optimization algorithms in solving high-dimensional problems and application potential in real-world optimization problems.