基于K-均值的“教”与“学”优化算法

在解决复杂多峰优化问题时,传统的"教"与"学"优化算法易于陷入局部搜索且优化效率较低.针对此问题,提出了一种基于K-均值的"教"与"学"优化改进算法,算法采用K-均值来降低种群规模,又针对"教"和"学"两个阶段进行相应改进,提高全局收敛速度;还加入了"变异"操作来避免算法陷入局部最优.实验对7个单峰值优化问题和2个有代表性的多峰值优化问题进行优化,并与手榴弹爆破算法和传统"教"与"学"优化算法进行比较,实验结果表明,该改进算法在单峰和多峰测试函数中,均能快速高效地寻得全局最优解,优于原始"教"与"学"优化算法.

Improved teaching-learning-based optimization algorithm based on K-means

For the complex multimodal optimization problems, the traditional Teaching-Learning-Based Optimization (TLBO) algorithm is easy to fall into local search and has low optima efficiency. In order to solve the problem, an improved TLBO algorithm based on K-means was proposed in this paper. It used the K-means to decide the population into small populations for reducing the population size and correspondingly improved the "teaching" and "learning" stages to improve the speed of global convergence. At the same time, the proposed algorithm added "mutation" operation to avoid the local optimum. In the experiments, seven unimodal and two multimodal optimization problems were optimized by the algorithm proposed in this paper. The optimization results were compared grenade explosion method and traditional TLBO algorithm. The experimental results show that the improved algorithm can quickly and efficiently find the globally optimal solution in both unimodal and multimodal functions and the improved algorithm is better than the traditional TLBO algorithm in the ability of searching the globally optimal solutions.