[HTML][HTML] Differential evolution: A recent review based on state-of-the-art works
Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin's theory of
evolution and has been studied extensively to solve different areas of optimisation and …
evolution and has been studied extensively to solve different areas of optimisation and …
QANA: Quantum-based avian navigation optimizer algorithm
Differential evolution is an effective and practical approach that is widely applied for solving
global optimization problems. Nevertheless, its effectiveness and scalability are decreased …
global optimization problems. Nevertheless, its effectiveness and scalability are decreased …
An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation
A recent meta-heuristic algorithm called Marine Predators Algorithm (MPA) is enhanced
using Opposition-Based Learning (OBL) termed MPA-OBL to improve their search efficiency …
using Opposition-Based Learning (OBL) termed MPA-OBL to improve their search efficiency …
[HTML][HTML] Hierarchical Harris hawks optimizer for feature selection
L Peng, Z Cai, AA Heidari, L Zhang, H Chen - Journal of Advanced …, 2023 - Elsevier
Introduction The main feature selection methods include filter, wrapper-based, and
embedded methods. Because of its characteristics, the wrapper method must include a …
embedded methods. Because of its characteristics, the wrapper method must include a …
Hybrid slime mould algorithm with adaptive guided differential evolution algorithm for combinatorial and global optimization problems
Abstract The Slime Mould Algorithm (SMA) is a recent metaheuristic inspired by the
oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may …
oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may …
Planning and optimization of microgrid for rural electrification with integration of renewable energy resources
Microgrids are an effective means to provide power to urban and rural communities.
Microgrid planning must anticipate both the system's economic feasibility and long-term …
Microgrid planning must anticipate both the system's economic feasibility and long-term …
An enhanced Archimedes optimization algorithm based on Local esca** operator and Orthogonal learning for PEM fuel cell parameter identification
Meta-heuristic optimization algorithms aim to tackle real world problems through maximizing
some specific criteria such as performance, profit, and quality or minimizing others such as …
some specific criteria such as performance, profit, and quality or minimizing others such as …
Recent methodology-based gradient-based optimizer for economic load dispatch problem
Economic load dispatch (ELD) in power system problems involves scheduling the power
generating units to minimize cost and satisfy system constraints. Although previous works …
generating units to minimize cost and satisfy system constraints. Although previous works …
Improved artificial bee colony algorithm-based path planning of unmanned autonomous helicopter using multi-strategy evolutionary learning
Z Han, M Chen, S Shao, Q Wu - Aerospace Science and Technology, 2022 - Elsevier
Aiming at producing a high-quality flight path for the unmanned autonomous helicopter with
multi-constraints, a path planning method is proposed based on the multi-strategy …
multi-constraints, a path planning method is proposed based on the multi-strategy …
The orb-weaving spider algorithm for training of recurrent neural networks
AS Mikhalev, VS Tynchenko, VA Nelyub, NM Lugovaya… - Symmetry, 2022 - mdpi.com
The quality of operation of neural networks in solving application problems is determined by
the success of the stage of their training. The task of learning neural networks is a complex …
the success of the stage of their training. The task of learning neural networks is a complex …