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

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