A key aspect of the design of evolutionary and swarm intelligence algorithms is studying
their performance. Statistical comparisons are also a crucial part which allows for reliable …

The simulation-driven metaheuristic algorithms have been successful in solving numerous
problems compared to their deterministic counterparts. Despite this advantage, the …

An original swarm-based, bio-inspired metaheuristic algorithm, named electric eel foraging
optimization (EEFO) is developed and tested in this work. EEFO draws inspiration from the …

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 …

The rapid expansion of information science has caused the issue of “the curse of
dimensionality”, which will negatively affect the performance of the machine learning model …

In the realm of solving complex optimization problems, it becomes crucial to fulfill multiple
objectives to achieve optimal system performance while adhering to various conditions and …

The African vultures optimization algorithm (AVOA) is a recently proposed metaheuristic
inspired by the African vultures' behaviors. Though the basic AVOA performs very well for …

This survey compiles ideas and recommendations from more than a dozen researchers with
different backgrounds and from different institutes around the world. Promoting best practice …

The Grey wolf optimizer (GWO) is a recently introduced popular swarm-intelligence-based
metaheuristic algorithm, compared to other algorithms, it has shown competitive …

In view of the shortcomings such as slow search speed, low optimization precision and
premature convergence of artificial hummingbird algorithm, an enhanced artificial …