  The set-union knapsack problem (SUKP) is a constrained composed optimization
problem. It is more difficulty for solving because values and weights depend on
items and elements respectively. In this paper, we present two self-adjusting
optimization algorithms for approximating SUKP from items and elements
perspective respectively. By analyzing the dynamic characters in the SUKP, we
design two types of self-adjusting repair and optimization operators that are
based on the different loading process. We use the novel
teaching-learning-based optimization algorithm (TLBO) to design a general
discrete framework (DTLBO) suitable for these two types of operators. In
addition, we introduce elite opposite search and natural selection mechanism
into DTLBO to furtherly improve the performance of the algorithm from the
perspective of population. Finally, we performed experimental comparisons on
benchmark sets to verify the effectiveness of the proposed algorithm. The
experimental results show that the item-based self-adjusting optimization
algorithm I-DTLBO is outstanding, and the algorithm is superior to the other
swarm intelligence methods for solving SUKP. IDTLBO algorithm reaches the upper
boundary of the current swarm intelligence algorithms for solving SUKP in 10
instances, and gotten new upper boundary in 15 instances. The algorithm E-DTLBO
based on element loading only perform slightly better on small and middle data
sets, but worse on large-scale instances. It shows that element-based design is
not suitable for solving SUKP.
