融合学习心理学的人类学习优化算法

Human learning optimization algorithm based on learning psychology

针对简单人类学习优化(SHLO)算法寻优精度低和收敛慢的问题,提出了一种融合学习心理学的人类学习优化算法(LPHLO)。首先,结合学习心理学中的小组学习(TBL)理论引入TBL算子,从而在个体经验、社会经验的基础上,增加了小组经验来对个体学习状态进行控制,避免算法早熟收敛;然后,结合记忆编码理论提出了动态调参策略,从而实现个体信息、社会信息、团队信息的有效融合,更好地平衡了算法局部探索和全局开发的能力。选取典型的组合优化难题——背包问题中的两种算例,即单约束背包问题、多约束背包问题进行仿真实验,实验结果表明,所提LPHLO与基本的SHLO算法、遗传算法(GA)和二进制粒子群优化(BPSO)算法等算法相比,在寻优精度和收敛速度方面更具优势,具有更好的解决实际问题的能力。

Aiming at the problems of low optimization accuracy and slow convergence of Simple Human Learning Optimization(SHLO)algorithm,a new Human Learning Optimization algorithm based on Learning Psychology(LPHLO)was proposed.Firstly,based on Team-Based Learning(TBL)theory in learning psychology,the TBL operator was introduced,so that on the basis of individual experience and social experience,team experience was added to control individual learning state to avoid the premature convergence of algorithm.Then,the memory coding theory was combined to propose the dynamic parameter adjustment strategy,thereby effectively integrating the individual information,social information and team information.And the abilities of the algorithm to explore locally and develop globally were better balanced.Two examples of knapsack problem of typical combinatorial optimization problems,0-1 knapsack problem and multi-constraint knapsack problem,were selected for simulation experiments.Experimental results show that,compared with the algorithms such as SHLO algorithm,Genetic Algorithm(GA)and Binary Particle Swarm Optimization(BPSO)algorithm,the proposed LPHLO has more advantages in optimization accuracy and convergence speed,and has a better ability to solve the practical problems.