多策略改进状态基优化算法及其工程应用

Multi-Strategy Improved State-Basis Optimization Algorithm and Its Engineering Application

摘要: 本文提出一种多策略改进状态基优化算法，用于改善原始状态基优化算法容易陷入局部最优、收敛速度差等问题。在初始化阶段引入Logistic-Tent混沌映射产生多样性更为丰富的种群；在资源获取和资源评估阶段引入思维创新策略，平衡算法的收敛精度和搜索能力。为测试改进算法的性能，在CEC2022基准函数测试集中进行仿真测试，实验结果证明了改进算法在收敛速度、求解精度和稳定性等方面得到了较大提升。此外，选用了经典的弹簧设计问题进行对比仿真实验，验证了算法具有优良的工程实用性。

Abstract: This paper proposes a multi-strategy improved state-based optimization algorithm to address the problems of local optima and poor convergence speed inherent in the original state-based optimization algorithm. In the initialization phase, a Logistic-Tent chaotic mapping is introduced to generate a more diverse population. In the resource acquisition and resource evaluation phases, innovative thinking strategies are introduced to balance the algorithm’s convergence accuracy and search capability. To test the performance of the improved algorithm, simulation tests were conducted on the CEC2022 benchmark function test set. Experimental results demonstrate that the improved algorithm achieves significant improvements in convergence speed, solution accuracy, and stability. Furthermore, a comparative simulation experiment was conducted using the classic spring design problem, verifying the algorithm’s excellent engineering applicability.