Evolutionary multi-mode slime mold optimization: a hyper-heuristic algorithm inspired by slime mold foraging behaviors

This paper proposes a novel hyper-heuristic algorithm termed evolutionary multi-mode slime mold optimization (EMSMO) for addressing continuous optimization problems. The architecture of a typical hyper-heuristic algorithm comprises two main components: the high-level component and the low-level comp...

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Bibliographic Details
Published in:	The Journal of supercomputing Vol. 80; no. 9; pp. 12186 - 12217
Main Authors:	Zhong, Rui, Zhang, Enzhi, Munetomo, Masaharu
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.06.2024 Springer Nature B.V
Subjects:	Brain research Compilers Computer Science Evolutionary algorithms Experiments Food Foraging behavior Genetic algorithms Heuristic Heuristic methods Interpreters Mathematical models Mold Nutrition Optimization Performance evaluation Processor Architectures Programming Languages Scheduling Search methods Slime Statistical analysis Improvement-based probabilistic selection function Hyper-heuristics Continuous optimization Bio-inspired optimization algorithm
ISSN:	0920-8542, 1573-0484
Online Access:	Get full text
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