The ant colony optimization (ACO) is the most exceptionally fundamental swarm-based
solver for realizing discrete problems. In order to make it also suitable for solving continuous …

The behavior of natural phenomena has become one of the most popular sources for
researchers to design optimization algorithms for scientific, computing and engineering …

The performance evaluation of a Photovoltaic (PV) system heavily relies on accurately
estimating the parameters based on its current—voltage relationships. However, due to the …

Adjusting the search behaviors of swarm-based algorithms during their execution is a
fundamental errand for addressing real-world global optimizing challenges. Along this line …

The purpose of this study is to utilize reinforcement learning in order to improve the
performance of the Sand Cat Swarm Optimization algorithm (SCSO). In this paper, we …

In this article, an effective metaheuristic algorithm named multi-trial vector-based differential
evolution (MTDE) is proposed. The MTDE is distinguished by introducing an adaptive …

Grey wolf optimizer (GWO) is a widespread metaphor-based algorithm based on the
enhanced variants of velocity-free particle swarm optimizer with proven defects and …

JADE is a differential evolution (DE) algorithm and has been shown to be very competitive in
comparison with other evolutionary optimization algorithms. However, it suffers from the …

In this work, a modified structure of the tilted integral derivative (TID) controller, ie an integral
derivative-tilted (ID-T) controller, is developed for the load frequency control issue of a multi …

This paper presents a new optimization algorithm called poor and rich optimization (PRO).
This algorithm is inspired by the efforts of the two groups of the poor and the rich to achieve …