Evolutionary multi-mode slime mold optimization: a hyper-heuristic algorithm inspired by slime mold foraging behaviorsThis 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 component.
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Design of fault tolerant networks: the slime mold approach [PDF]
, 2011 Houbraken, Maarten
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Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing. [PDF]
PLoS One, 2023 Zhang X, Liu Q, Bai X.
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Small-Signal Stability Analysis and MOSMA-Based Optimization Control Strategy of OWF with MMC-HVDC Grid Connection. [PDF]
Sensors (Basel), 2023 Zheng J, Li H, Zhang B, Li Q.
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A Metric-Driven Evaluation Framework for Remaining Useful Life Prognosis with Quantified Uncertainty. [PDF]
Sensors (Basel)Vashishtha G, Chauhan S, Ertarğın M.
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Research on Supply Chain Financial Risk Prevention Based on Machine Learning. [PDF]
Comput Intell Neurosci, 2023 Lei Y, Qiaoming H, Tong Z.
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An improved artemisinin algorithm for task allocation in heterogeneous robot systems for chemical inspection. [PDF]
Sci RepLi C, Liu Q, Yin M, Lang X, Bo G.
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The blob algorithm: a network approach to residual sleepiness in treated obstructive sleep apnea. [PDF]
SleepWilliams MK, Varga AW, Ayappa I.
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[a speculative and incomplete] Jamming Playbook: Re/Decomposing the Slime Mold Algorithm
The strange, abject allure of slime mold creeps across fields, sporulating into science fiction, artistic practice, computing, and beyond. Captured by Western logics, slime mold’s pathway-finding ability has been mathematized into the Slime Mold Algorithm (SMA), a tool used in solving optimization problems.
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