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 …

Metaheuristic algorithms are widely known as efficient solutions for solving problems of
optimization. These algorithms supply powerful instruments with significant engineering …

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 …

This article provides a survey on the highly popular metaheuristic framework, the adaptive
large neighborhood search (ALNS). The basic concepts of ALNS are discussed in this …

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 …

This survey is an effort to provide a research repository and a useful reference for
researchers to guide them when planning to develop new Nature-inspired Algorithms …

Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary
optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed …

Traveling salesman and vehicle routing problems with their variants, as classic
combinatorial optimization problems, have attracted considerable attention for decades of …

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 …

One of the most available energy sources in the world is solar energy, while in the category
of renewable and nonrenewable energies is in the first group. Power generation of a …