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Continuous metaheuristics for binary optimization problems: An updated systematic literature review
For years, extensive research has been in the binarization of continuous metaheuristics for
solving binary-domain combinatorial problems. This paper is a continuation of a previous …
solving binary-domain combinatorial problems. This paper is a continuation of a previous …
Parameter extraction of solar photovoltaic models using queuing search optimization and differential evolution
Given the photovoltaic (PV) model's multi-model and nonlinear properties, extracting its
parameters is a difficult problem to solve. Furthermore, because of the features of the …
parameters is a difficult problem to solve. Furthermore, because of the features of the …
Effective feature selection strategy for supervised classification based on an improved binary aquila optimization algorithm
Feature Selection (FS) is considered a crucial step in machine learning and data mining
tasks, which facilitates minimizing the direct consequence of redundant and irrelevant …
tasks, which facilitates minimizing the direct consequence of redundant and irrelevant …
Improved Binary Meerkat Optimization Algorithm for efficient feature selection of supervised learning classification
Feature selection (FS) is a crucial step in machine learning and data mining projects. It aims
to remove redundant and uncorrelated features, thus improving the accuracy of models …
to remove redundant and uncorrelated features, thus improving the accuracy of models …
Solving engineering optimization problems based on multi-strategy particle swarm optimization hybrid dandelion optimization algorithm
W Tang, L Cao, Y Chen, B Chen, Y Yue - Biomimetics, 2024 - mdpi.com
In recent years, swarm intelligence optimization methods have been increasingly applied in
many fields such as mechanical design, microgrid scheduling, drone technology, neural …
many fields such as mechanical design, microgrid scheduling, drone technology, neural …
A Systematic Review of Wind Driven Optimization Algorithms and Their Variants
LL Mao, AM Zain, KQ Zhou, F Qin, FL Wang - IEEE Access, 2024 - ieeexplore.ieee.org
Wind Driven Optimization (WDO) Algorithm is a novel metaheuristic algorithm inspired by
the continuous flow of air resulting from differences in air pressure until the air reaches a …
the continuous flow of air resulting from differences in air pressure until the air reaches a …
[PDF][PDF] Deep learning model based on ResNet-50 for beef quality classification
Food quality measurement is one of the most essential topics in agriculture and industrial
fields. To classify healthy food using computer visual inspection, a new architecture was …
fields. To classify healthy food using computer visual inspection, a new architecture was …
A weighted-sum chaotic sparrow search algorithm for interdisciplinary feature selection and data classification
In today's data-driven digital culture, there is a critical demand for optimized solutions that
essentially reduce operating expenses while attempting to increase productivity. The …
essentially reduce operating expenses while attempting to increase productivity. The …
An improved Differential evolution with Sailfish optimizer (DESFO) for handling feature selection problem
As a preprocessing for machine learning and data mining, Feature Selection plays an
important role. Feature selection aims to streamline high-dimensional data by eliminating …
important role. Feature selection aims to streamline high-dimensional data by eliminating …
S‐shaped and V‐shaped binary African vulture optimization algorithm for feature selection
K Balakrishnan, R Dhanalakshmi… - Expert …, 2022 - Wiley Online Library
The African vulture optimization algorithm (AVOA) is a recently developed metaheuristic
algorithm that imitates the eating and movement patterns of authentic African vultures. AVOA …
algorithm that imitates the eating and movement patterns of authentic African vultures. AVOA …