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Hybrid approaches to optimization and machine learning methods: a systematic literature review
Notably, real problems are increasingly complex and require sophisticated models and
algorithms capable of quickly dealing with large data sets and finding optimal solutions …
algorithms capable of quickly dealing with large data sets and finding optimal solutions …
Machine and deep learning for resource allocation in multi-access edge computing: A survey
With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
Greylag goose optimization: nature-inspired optimization algorithm
Nature-inspired metaheuristic approaches draw their core idea from biological evolution in
order to create new and powerful competing algorithms. Such algorithms can be divided into …
order to create new and powerful competing algorithms. Such algorithms can be divided into …
[HTML][HTML] Optimization, validation and analyses of a hybrid PV-battery-diesel power system using enhanced electromagnetic field optimization algorithm and ε …
This study introduces and assesses a hybrid renewable energy system tailored for a
practical, off-grid location. The integrated power system comprises a diesel generator …
practical, off-grid location. The integrated power system comprises a diesel generator …
Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism
In this paper, a parameter adaptation-based ant colony optimization (ACO) algorithm based
on particle swarm optimization (PSO) algorithm with the global optimization ability, fuzzy …
on particle swarm optimization (PSO) algorithm with the global optimization ability, fuzzy …
Multi-strategy particle swarm and ant colony hybrid optimization for airport taxiway planning problem
As the connecting hub of the airport runways and gates, the taxiway plays a very important
role in the rational allocation and utilization of the airport resources. In this paper, a multi …
role in the rational allocation and utilization of the airport resources. In this paper, a multi …
An improved quantum-inspired differential evolution algorithm for deep belief network
Deep belief network (DBN) is one of the most representative deep learning models.
However, it has a disadvantage that the network structure and parameters are basically …
However, it has a disadvantage that the network structure and parameters are basically …
A novel gate resource allocation method using improved PSO-based QEA
With the continuous and rapid growth of air traffic demand, gate resource becomes a major
bottleneck restricting airport development. Rational gate allocation is regarded as one of the …
bottleneck restricting airport development. Rational gate allocation is regarded as one of the …
Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy
Although the continuous version of ant colony optimizer (ACOR) has been successfully
applied to various problems, there is room to boost its stability and improve convergence …
applied to various problems, there is room to boost its stability and improve convergence …
Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies
The first powerful variant of the Harris hawks optimization (HHO) is proposed in this work.
HHO is a recently developed swarm-based stochastic algorithm that has previously shown …
HHO is a recently developed swarm-based stochastic algorithm that has previously shown …