关键词: 简单人类学习优化算法, 学习心理学, 学习策略, 小组学习算子, 动态调参策略

Abstract:

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.

Key words: Simple Human Learning Optimization (SHLO) algorithm, learning psychology, learning strategy, Team-Based Learning (TBL) operator, dynamic parameter adjustment strategy