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 …

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

Nature computing has evolved with exciting performance to solve complex real-world
combinatorial optimization problems. These problems span across engineering, medical …

This paper proposes a new meta-heuristic algorithm inspired by horses' herding behavior for
high-dimensional optimization problems. This method, called the Horse herd Optimization …

Efficient task scheduling is considered as one of the main critical challenges in cloud
computing. Task scheduling is an NP-complete problem, so finding the best solution is …

This paper proposes an optimisation algorithm called Grasshopper Optimisation Algorithm
(GOA) and applies it to challenging problems in structural optimisation. The proposed …

Many population-dependent solutions have recently been suggested. Despite their
widespread adoption in many applications, we are still researching using suggested …

In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame
Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method …

The mode number and mode frequency bandwidth control parameter (or quadratic penalty
term) have significant effects on the decomposition results of the variational mode …

This paper proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The
ALO algorithm mimics the hunting mechanism of antlions in nature. Five main steps of …