Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2021, Volume and Issue: 388, P. 114194 - 114194

Published: Nov. 9, 2021

Language: Английский

---

Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies

Future Generation Computer Systems, Journal Year: 2020, Volume and Issue: 111, P. 175 - 198

Published: April 11, 2020

Language: Английский

---

Boosted binary Harris hawks optimizer and feature selection

Engineering With Computers, Journal Year: 2020, Volume and Issue: 37(4), P. 3741 - 3770

Published: May 13, 2020

Language: Английский

---

Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis

Knowledge-Based Systems, Journal Year: 2020, Volume and Issue: 214, P. 106728 - 106728

Published: Dec. 31, 2020

Language: Английский

---

An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection

Expert Systems with Applications, Journal Year: 2021, Volume and Issue: 176, P. 114778 - 114778

Published: March 5, 2021

Language: Английский

---

Harris hawks optimization: a comprehensive review of recent variants and applications

Neural Computing and Applications, Journal Year: 2021, Volume and Issue: 33(15), P. 8939 - 8980

Published: Feb. 3, 2021

Language: Английский

---

Hybridised Artificial Neural Network Model with Slime Mould Algorithm: A Novel Methodology for Prediction of Urban Stochastic Water Demand

Water, Journal Year: 2020, Volume and Issue: 12(10), P. 2692 - 2692

Published: Sept. 26, 2020

Urban water demand prediction based on climate change is always challenging for utilities because of the uncertainty that results from a sudden rise in due to stochastic patterns climatic factors. For this purpose, novel combined methodology including, firstly, data pre-processing techniques were employed decompose time series and factors by using empirical mode decomposition identifying best model input via tolerance avoid multi-collinearity. Second, artificial neural network (ANN) was optimised an up-to-date slime mould algorithm (SMA-ANN) predict medium term signal monthly urban demand. Ten over 16 years used simulate The reveal SMA outperforms multi-verse optimiser backtracking search error scale. performance hybrid SMA-ANN better than ANN (stand-alone) range statistical criteria. Generally, yields accurate with coefficient determination 0.9 mean absolute relative 0.001. This study can assist local managers efficiently manage present system plan extensions accommodate increasing

Language: Английский

---

An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation

Knowledge-Based Systems, Journal Year: 2021, Volume and Issue: 229, P. 107348 - 107348

Published: July 30, 2021

Language: Английский

---

An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 149, P. 106075 - 106075

Published: Sept. 6, 2022

Language: Английский

---

An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm

Expert Systems with Applications, Journal Year: 2021, Volume and Issue: 185, P. 115651 - 115651

Published: July 30, 2021

Language: Английский

---