多分支混沌变异的头脑风暴优化算法

头脑风暴优化算法是一种受人类群体行为启发的新型群智能优化算法。该算法通过模拟人类使用头脑风暴创造性解决问题的行为，在解空间中分析个体分布，并使用变异生成新个体，多次迭代求得最优解，具有较高的鲁棒性和自适应能力。针对头脑风暴优化算法精度较差、易陷入局部最优导致早熟收敛的缺陷，提出了一种多分支混沌变异的头脑风暴优化算法。该算法选取8种混沌映射，设计了一种多分支混沌变异算子。当原始算法陷入局部最优时，使用多分支混沌变异生成新个体，利用多种混沌运动的遍历性、随机性和多样性，扩大了混沌空间的范围，增强了算法全局搜索的能力。对10个经典测试函数的10、20、30维问题进行测试，并与原始头脑风暴优化算法、粒子群优化算法、遗传算法和布谷鸟搜索算法进行对比，实验结果表明，所提出的算法可以有效避免陷入局部最优，具有更高的稳定性和全局搜索能力。

Brain Storm Optimization Based on Multi-Branch Chaotic Mutation

Brain storm optimization is a newly proposed swarm intelligence optimization algorithm inspired by human group behavior. This algorithm simulates the human behavior of using brain storm to solve problems creatively, analyzes the individual distribution in the solution space, uses mutation to generate new individuals to obtain the optimal solution through multiple iterations, and obtains high robustness and adaptive ability. Aiming at the defects of brain storm optimization algorithm, which has poor accuracy and the tendency to fall into local optimum leading to premature convergence, a brain storm optimization algorithm based on multi-branch chaotic mutation is proposed. This algorithm selects eight chaotic maps to design a multi-branch chaotic mutation operator. When the original algorithm falls into the local optimum, new individuals are generated by using multi-branch chaotic mutation which uses the ergodicity, randomness and diversity of multiple chaotic motions to expand the scope of the chaotic space and enhance the global searching ability of the algorithm. The proposed algorithm is verified respectively by the 10, 20, 30 dimensional problems of 10 classic test functions, and compared with original brain storm optimization algorithm, particle swarm optimization algorithm, genetic algorithm and cuckoo search algorithm. The experimental results show that the proposed algorithm can effectively avoid falling into local optimum, and has higher stability and global search ability.