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 …

In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …

Large language models (LLMs) have demonstrated tremendous capabilities in solving
complex tasks, from quantitative reasoning to understanding natural language. However …

Metaheuristics are an impressive area of research with extremely important improvements in
the solution of intractable optimization problems. Major advances have been made since the …

In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …

In this review paper, JAYA algorithm, which is a recent population-based algorithm is
intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from …

It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

This paper presents a framework to tackle combinatorial optimization problems using neural
networks and reinforcement learning. We focus on the traveling salesman problem (TSP) …

Metaheuristic algorithms are computational intelligence paradigms especially used for
sophisticated solving optimization problems. This chapter aims to review of all …

Abstract Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been
successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally …