A comprehensive survey on recent metaheuristics for feature selection
Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …
preprocessing due to the ever-increasing sizes in actual data. There have been many …
Performance assessment of the metaheuristic optimization algorithms: an exhaustive review
The simulation-driven metaheuristic algorithms have been successful in solving numerous
problems compared to their deterministic counterparts. Despite this advantage, the …
problems compared to their deterministic counterparts. Despite this advantage, the …
[HTML][HTML] Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms
Metaheuristics are popularly used in various fields, and they have attracted much attention
in the scientific and industrial communities. In recent years, the number of new metaheuristic …
in the scientific and industrial communities. In recent years, the number of new metaheuristic …
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and …
Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …
successful techniques in machine learning. Recently, the number of ensemble-based …
A particle swarm optimization algorithm for mixed-variable optimization problems
Many optimization problems in reality involve both continuous and discrete decision
variables, and these problems are called mixed-variable optimization problems (MVOPs) …
variables, and these problems are called mixed-variable optimization problems (MVOPs) …
Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis
Moth flame optimization (MFO) is a swarm-based algorithm with mediocre performance and
marginal originality proposed in recent years. It tried to simulate the fantasy navigation mode …
marginal originality proposed in recent years. It tried to simulate the fantasy navigation mode …
Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting
There are several major available renewable energies, such as wind power which can be
considered one of the most potential energy resources. Thus, wind power is a vital green …
considered one of the most potential energy resources. Thus, wind power is a vital green …
A benchmark-suite of real-world constrained multi-objective optimization problems and some baseline results
Abstract Generally, Synthetic Benchmark Problems (SBPs) are utilized to assess the
performance of metaheuristics. However, these SBPs may include various unrealistic …
performance of metaheuristics. However, these SBPs may include various unrealistic …
A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models
Photovoltaic (PV) generation systems are vital to the utilization of the sustainable and
pollution-free solar energy. However, the parameter estimation of PV systems remains very …
pollution-free solar energy. However, the parameter estimation of PV systems remains very …
A better balance in metaheuristic algorithms: Does it exist?
The constant development of new metaheuristic algorithms has led to a saturation in the
field of stochastic search. There are now hundreds of different algorithms that can be used to …
field of stochastic search. There are now hundreds of different algorithms that can be used to …