As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …

The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

Over the past years, many efforts have been accomplished to achieve fast and accurate
meta-heuristic algorithms to optimize a variety of real-world problems. This study presents a …

This study proposes a novel artificial protozoa optimizer (APO) that is inspired by protozoa in
nature. The APO mimics the survival mechanisms of protozoa by simulating their foraging …

In this paper, a novel evolutionary optimization algorithm, named Partial Reinforcement
Optimizer (PRO), is introduced. The major idea behind the PRO comes from a psychological …

This paper introduces a novel physical-inspired metaheuristic algorithm called “Light
Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the …

This paper introduces a new metaheuristic technique known as the Greater Cane Rat
Algorithm (GCRA) for addressing optimization problems. The optimization process of GCRA …

This study presents a powerful and new modeling variant of bio-inspired algorithms, namely,
the Ivy algorithm (IVYA), drawn from the growth patterns of Ivy plants. The algorithm …

Meta-heuristic algorithms have been widely used to solve different optimization problems.
There have always been ongoing efforts to develop new and efficient algorithms. In this …

Thermal system optimization is always a challenging task due to several constraints and
critical concepts of thermo-hydraulic aspects. Heat exchangers are one of those devices that …