Abstract: The Brain Storm Optimization Algorithm (BSO) is a swarm intelligence optimization algorithm proposed to simulate human brain thinking activities. Aiming at the problems of poor accuracy and weak optimization ability of traditional brainstorming optimization algorithms, which are prone to falling into local optima, an Improved Brain Storm Optimization Algorithm (IBSO) that integrates independent thinking and local escape is proposed. Firstly, an independent thinking strategy is proposed, which adds a threshold to determine whether to execute the independent thinking strategy when the algorithm is stuck in a local optimal solution. When the algorithm falls into a local optimum and cannot obtain a better solution, it will use an independent thinking strategy to find a new position, assisting the algorithm in seeking a better solution to escape from the local optimum. Secondly, the Local Escaping Operator (LEO) strategy was adopted to enhance the algorithm's global exploration capability and improve its search efficiency. Test the optimization performance of IBSO algorithm using CEC2014 benchmark test function and CEC2020 benchmark test function, and conduct comparative experiments with 8 optimization algorithms. The results indicate that the improved algorithm has stronger optimization ability, higher stability, and global search capability. Finally, the latest engineering problem evaluation indicators were used to conduct testing experiments on two engineering problems, namely the design of a three bar truss and the design of tension/compression springs, further verifying the practicality of the IBSO algorithm in engineering problems.

Key words: brain storm optimization algorithm, local escaping operator, benchmark function, engineering problem

摘要： 头脑风暴优化算法（Brain Storm Optimization Algorithm, BSO）是一种模拟人脑思维活动所提出的群智能优化算法。针对传统头脑风暴优化算法精度较差、寻优能力弱，易陷入局部最优等问题，提出了融合独立思维与局部逃逸的头脑风暴优化算法（Improved Brain Storm Optimization Algorithm, IBSO）。首先，提出了一种独立思维策略，当算法陷入局部最优解停滞时，加入了一个阈值用于判断是否需要执行独立思维策略。当算法陷入局部最优导致无法获得更优解时，算法会通过独立思维策略寻找一个新的位置，协助算法寻求更优解以跳出局部最优。其次，采用了局部逃逸策略（Local Escaping Operator, LEO），加强了算法全局探索能力，使得算法的搜索效率更强。通过CEC2014基准测试函数和CEC2020基准测试函数来测试IBSO算法的优化性能，并与8种优化算法进行对比实验。结果表明，所改进的算法寻优能力更强，具有更高的稳定性和全局搜索能力。最后，采用最新的工程问题评价指标对三杆桁架设计和拉伸/压缩弹簧设计两种工程问题进行测试实验，进一步验证了IBSO算法在工程问题中的实用性。

关键词: 头脑风暴优化算法, 局部逃逸策略, 基准测试函数, 工程问题