Particle swarm optimization: A comprehensive survey
Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms
in the literature. Although the original PSO has shown good optimization performance, it still …
in the literature. Although the original PSO has shown good optimization performance, it still …
Overview of histone modification
Epigenetics is the epi-information beyond the DNA sequence that can be inherited from
parents to offspring. From years of studies, people have found that histone modifications …
parents to offspring. From years of studies, people have found that histone modifications …
Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study
The whale optimization algorithm (WOA) is a prominent problem solver which is broadly
applied to solve NP-hard problems such as feature selection. However, it and most of its …
applied to solve NP-hard problems such as feature selection. However, it and most of its …
Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection
J Hu, H Chen, AA Heidari, M Wang, X Zhang… - Knowledge-Based …, 2021 - Elsevier
This research's genesis is in two aspects: first, a guaranteed solution for mitigating the grey
wolf optimizer's (GWO) defect and deficiencies. Second, we provide new open-minding …
wolf optimizer's (GWO) defect and deficiencies. Second, we provide new open-minding …
Dispersed foraging slime mould algorithm: Continuous and binary variants for global optimization and wrapper-based feature selection
J Hu, W Gui, AA Heidari, Z Cai, G Liang, H Chen… - Knowledge-Based …, 2022 - Elsevier
The slime mould algorithm (SMA) is a logical swarm-based stochastic optimizer that is easy
to understand and has a strong optimization capability. However, the SMA is not suitable for …
to understand and has a strong optimization capability. However, the SMA is not suitable for …
A survey on new generation metaheuristic algorithms
Metaheuristics are an impressive area of research with extremely important improvements in
the solution of intractable optimization problems. Major advances have been made since the …
the solution of intractable optimization problems. Major advances have been made since the …
Simulated annealing-based dynamic step shuffled frog lea** algorithm: Optimal performance design and feature selection
The shuffled frog lea** algorithm is a new optimization algorithm proposed to solve the
combinatorial optimization problem, which effectively combines the memetic algorithm …
combinatorial optimization problem, which effectively combines the memetic algorithm …
Reinforcement learning-based control using Q-learning and gravitational search algorithm with experimental validation on a nonlinear servo system
This paper presents a novel Reinforcement Learning (RL)-based control approach that uses
a combination of a Deep Q-Learning (DQL) algorithm and a metaheuristic Gravitational …
a combination of a Deep Q-Learning (DQL) algorithm and a metaheuristic Gravitational …
BEPO: A novel binary emperor penguin optimizer for automatic feature selection
Abstract Emperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently
developed and illustrates the emperor penguin's huddling behaviour. However, the original …
developed and illustrates the emperor penguin's huddling behaviour. However, the original …
An efficient binary salp swarm algorithm with crossover scheme for feature selection problems
Searching for the (near) optimal subset of features is a challenging problem in the process of
feature selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior …
feature selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior …