混合策略改进的回旋镖气动椭圆优化算法及其应用

Hybrid Strategy Improved Boomerang Aerodynamic Ellipse Optimization Algorithm and Its Application

摘要: 针对回旋镖气动椭圆优化算法(BAEO)在复杂搜索空间中易陷入局部最优、种群初始化分布不均等问题，提出了一种混合策略改进的回旋镖气动椭圆优化算法(IBAEO)。首先，采用Logistic-Tent复合混沌映射进行种群初始化，提升初始解的多样性与均匀性。其次，引入思维创新策略(TIS)，通过信息事件、知识深度与想象力三个核心算子的协同作用，利用正切算子的非线性扰动增强算法跳出局部最优的能力。与此同时，将上述改进与BAEO原有的空气动力椭圆局部挖掘机制融合为双阶段优化框架，通过精英记忆库动态维护与二次认知扰动，实现全局探索与局部开发的精细平衡。最后，通过CEC2022测试函数及焊接梁设计工程验证表明，IBAEO比原始算法及其他对比算法收敛更快、稳定性更高，能有效降低工程成本，验证了其在复杂非线性约束下的高稳定性和工程实用性。

Abstract: In order to solve the problems of boomerang aerodynamic ellipse optimization algorithm (BAEO) in complex search space, it is easy to fall into the local optimal and the population initial distribution is not equal, a hybrid strategy improved Boomerang aerodynamic Ellipse optimization algorithm (IBAEO) was proposed. Firstly, a Logistic-Tent composite chaotic mapping is used for population initialization to enhance the diversity and uniformity of the initial solutions. Secondly, the Thinking Innovation Strategy (TIS) is introduced, and through the collaborative effect of three core operators—information events, knowledge depth, and imagination—the tangent operator’s nonlinear perturbation is utilized to enhance the algorithm’s ability to escape from local optima. At the same time, the above improvements are integrated with the original air dynamic ellipse local mining mechanism of BAEO into a two-stage optimization framework. Through the dynamic maintenance of the elite memory bank and the secondary cognitive perturbation, a fine balance between global exploration and local exploitation is achieved. Finally, the verification through CEC2022 test functions and the welding beam design engineering shows that IBAEO converges faster and has higher stability than the original algorithm and other comparison algorithms, and can effectively reduce engineering costs, verifying its high stability and engineering practicability under complex nonlinear constraints.