Real-world engineering design problems are widespread in various research disciplines in
both industry and industry. Many optimization algorithms have been employed to address …

In recent years, several multi-method and multi-operator-based algorithms have been
proposed for solving optimization problems. Generally, their performance is better than other …

In the last few decades, genetic algorithms (GAs) demonstrated to be an effective approach
for solving real-world optimization problems. However, it is known that, in presence of a …

In view of the shortcomings of the whale optimization algorithm (WOA), such as slow
convergence speed, low accuracy, and easy to fall into local optimum, an improved whale …

The performance of differential evolution (DE) significantly depends on the settings of
mutation strategies and control parameters. Inappropriate settings may cause an imbalance …

During last two decades, Differential Evolution (DE) proved to be one of the most popular
and successful evolutionary algorithms for solving global optimization problems over …

In the past few decades, a lot of optimization methods have been applied in estimating the
parameter of photovoltaic (PV) models and obtained better results, but these methods still …

The differential evolution (DE) algorithm is a heuristic random search algorithm that
optimizes the problem based on population evolution. It has been widely studied for its …

Differential evolution (DE) is a simple and effective stochastic search algorithm, but its
convergence speed and population diversity often decline catastrophically with the evolution …

Differential evolution (DE) is recognized as a simplistic yet robust evolutionary algorithm: it
has been utilized to tackle different challenging optimization problems in various science …