融合头脑风暴思想的教与学优化算法

针对教与学优化（TLBO）算法在求解高维问题时表现出的收敛速度慢、解精度低、易陷入于局部最优的问题，提出了一种融合头脑风暴思想的改进教与学优化算法（ITLBOBSO）。在该算法中设计了一种新的“学”算子，并以其替换TLBO算法中的“学”。该算法在种群的迭代过程中，当前个体首先执行“教”算子。随后，在种群中随机选择两个个体，令其中优秀的个体与当前个体执行头脑风暴式学习，提升当前个体的状态。为了赋予算法早期良好的探索能力和后期对新解的开发能力，在该算子的公式中引入柯西变异和一个与迭代次数关联的随机参数。进行的一系列的仿真实验表明，与TLBO算法相比，所提算法在11个Benchmark函数上的解精度、鲁棒性和收敛速度都有大幅度提升。在2个约束工程优化问题上，ITLBOBSO所求得的耗费成本比TLBO算法降低了4个百分点。由此验证了所提出的机制对克服TLBO弱点的有效性，所提算法适合用来求解较高维度的连续优化问题。

Improved teaching & learning based optimization with brain storming

Concerning the problems that Teaching & Learning Based Optimization (TLBO) algorithm has slow convergence rate and low accuracy, and it is easy to be trapped into local optimum in solving high-dimensional problems, an Improved TLBO algorithm with Brain Storming Optimization (ITLBOBSO) was proposed. In this algorithm, a new “learning”operator was designed and applied to replace the origin “learning” in the TLBO. In the iteration process of the population, the “teaching” operator was executed by the current individual. Then, two individuals were selected randomly from the population, and brain storming learning was executed by the better one of the above and the current individual to improve the state of the current individual. Cauchy mutation and a random parameter associated with the iterations were introduced in the formula of this operator to improve the exploration ability in early stage and the exploitation ability for new solutions in later stage of the algorithm. In a series of simulation experimentations, compared with TLBO, the proposed algorithm has large improvements of solution accuracy, robustness and convergence speed on 11 benchmark functions. The experimental results on two constrained engineering optimization problems show that compared to TLBO algorithm, ITLBOBSO reduces the total cost by 4 percentage points, which proves the effectiveness of the proposed mechanism on overcoming the weakness of TLBO algorithm. The proposed algorithm is suitable for solving high dimensional continuous optimization problems.