Memory Elements

TS is considered to be one of the meta-heuristic algorithms. It is used to solve hard optimization problems of which classical methods often encounter grea<sup>t</sup> difficulty [41]. It depends on memory structures rather than memoryless methods based on semi random processes implementing a form of sampling. The MHTSASM is an advanced TS that uses responsive and intelligent memory. It uses multiple memory elements such as EL, TL, and ASM in order to minimize search time and focus on promising regions.

EL: Elite list stores best visited solutions.

TL: Tabu list stores recently visited solutions.

ASMv: Feature partitions visitation matrix. It is a matrix that partitions each feature into p partitions, and it is stored as a matrix of size K × p × d. It stores the number of visits of each partition for all generated centers.

ASMu: Feature improvement update matrix. It has the same size of ASMv and stores the number of obtained improvements of objective functions while updating the centers.

MHTSASM uses both explicit and attributive memories. Explicit memory records complete solutions such as EL and TL. Attributive memory stores information about features of each solution such as ASMv and ASMu. MHTSASM uses memory elements to guide intensification and diversification strategies.
