Results 101 to 110 of about 2,751,102 (125)

An Enhanced Slime Mould Algorithm Based on Best-Worst Management for Numerical Optimization Problems. [PDF]

Biomimetics (Basel)
Li T, Meng H, Wang D, Fu B, Shao Y, Liu Z.   +5 more
europepmc   +1 more source

Optimization of a real municipal sewage treatment plant using CRFSMA algorithm and a mathematical model. [PDF]

Heliyon
Lu C, Chen Y, Eskandarpour B, Alnowibet KA.   +3 more
europepmc   +1 more source

Optimizing gait template generation from variable-length data: a dynamic dimension warping approach. [PDF]

Sci Rep
Jin D, Liu Y, Song Q, Guan Z, Ma X, Liu Y, Wu D.   +6 more
europepmc   +1 more source

HG-SMA: hierarchical guided slime mould algorithm for smooth path planning

Artificial Intelligence Review, 2023
Gang Hu 0002, Bo Du 0008, Guo Wei 0004
openaire   +3 more sources

Slime Mould Algorithm: A New Method for Stochastic Optimization [PDF]

Future Generation Computer Systems, 2020
In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based on the oscillation mode of slime mould in nature.
Ali Asghar Heidari, Seyedali Mirjalili, Huiling Chen   +2 more
exaly   +2 more sources

Boosted Local Dimensional Mutation and All-Dimensional Neighborhood Slime Mould Algorithm for Feature Selection

Neurocomputing, 2023
The slime mould algorithm (SMA) is a population-based optimization algorithm that mimics the foraging behavior of slime moulds with a simple structure and few hyperparameters.
Ali Asghar Heidari, Eatedal Alabdulkareem, Huiling Chen   +2 more
exaly   +2 more sources

Identifying and Estimating Solar Cell Parameters Using an Enhanced Slime Mould Algorithm [PDF]

Optik
This study proposed an enhanced slime mould algorithm (ESMA) for identifying the solar cells’ parameters for five photovoltaic (PV) models, making two modifications to the original slime mould algorithm (SMA).
Mohd Ashraf Ahmad
exaly   +2 more sources

MO-AI-SMA: A Multi-Objective Self-Adaptive Improved Slime Mould Algorithm for Dynamic UAV Path Planning in Complex and Uncertain Environments

Real-world unmanned aerial vehicle (UAV) path planning requires simultaneous optimisation of multiple conflicting objectives including flight distance, energy consumption, and obstacle-safety margins, in environments characterised by moving obstacles and time-varying wind fields.
Fuqiang Lu, Mathias Mankoe, Bihua Ling
openaire   +1 more source

Slime Mould Algorithm (SMA) and Adaptive Neuro-Fuzzy Inference (ANFIS)-Based Energy Management of FCHEV Under Uncertainty

IETE Journal of Research, 2023
S. Sridharan, N. Shanmugasundaram, E. Anna Devi, V. Vasan Prabhu, P. Velmurugan   +4 more
openaire   +1 more source

Decentralized Trading of High-Proportion Renewable Energy Microgrid Based on Master-Slave Game and Slime Mould Algorithm (SMA)

2022
Zhengguang Liu, Qinyue Tan, Jiajing Rui, Yueming Ding, Di Xiong, Wenqiang Shang, Bowen Pang, Dingyi Kang   +7 more
openaire   +1 more source