Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation
L Liu, D Zhao, F Yu, AA Heidari, C Li, J Ouyang… - Computers in Biology …, 2021 - Elsevier
This paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on
swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a …
swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a …
Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation
L Liu, D Zhao, F Yu, AA Heidari, J Ru, H Chen… - Computers in Biology …, 2021 - Elsevier
Breast cancer is one of the most dangerous diseases for women's health, and it is imperative
to provide the necessary diagnostic assistance for it. The medical image processing …
to provide the necessary diagnostic assistance for it. The medical image processing …
[HTML][HTML] Migration-based moth-flame optimization algorithm
Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that
demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO …
demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO …
[HTML][HTML] An improved moth-flame optimization algorithm with adaptation mechanism to solve numerical and mechanical engineering problems
Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths
toward the light source is an effective approach to solve global optimization problems …
toward the light source is an effective approach to solve global optimization problems …
[HTML][HTML] Hybridizing of whale and moth-flame optimization algorithms to solve diverse scales of optimal power flow problem
The optimal power flow (OPF) is a practical problem in a power system with complex
characteristics such as a large number of control parameters and also multi-modal and non …
characteristics such as a large number of control parameters and also multi-modal and non …
A hybrid moth flame optimization algorithm for global optimization
Abstract The Moth Flame Optimization (MFO) algorithm shows decent performance results
compared to other meta-heuristic algorithms for tackling non-linear constrained global …
compared to other meta-heuristic algorithms for tackling non-linear constrained global …
Enhanced Gaussian bare-bones grasshopper optimization: mitigating the performance concerns for feature selection
As a recent meta-heuristic algorithm, the uniqueness of the grasshopper optimization
algorithm (GOA) is to imitate the biological features of grasshoppers for single-objective …
algorithm (GOA) is to imitate the biological features of grasshoppers for single-objective …
An improved moth flame optimization algorithm based on modified dynamic opposite learning strategy
Moth flame optimization (MFO) algorithm is a relatively new nature-inspired optimization
algorithm based on the moth's movement towards the moon. Premature convergence and …
algorithm based on the moth's movement towards the moon. Premature convergence and …
[HTML][HTML] MFO-SFR: an enhanced moth-flame optimization algorithm using an effective stagnation finding and replacing strategy
Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is
widely used to solve different optimization problems. However, MFO and its variants …
widely used to solve different optimization problems. However, MFO and its variants …
MFeature: towards high performance evolutionary tools for feature selection
Y Xu, H Huang, AA Heidari, W Gui, X Ye, Y Chen… - Expert Systems with …, 2021 - Elsevier
Feature selection commonly refers to a process of using the candidate algorithm to detect
the optimal feature sets during the preprocessing steps in machine learning and data …
the optimal feature sets during the preprocessing steps in machine learning and data …