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 …

For years, extensive research has been in the binarization of continuous metaheuristics for
solving binary-domain combinatorial problems. This paper is a continuation of a previous …

This paper introduces a new variant of the metaheuristic algorithm based on the naked mole
rat (NMR) algorithm, called the Q-learning naked mole rat algorithm (QL-NMR), for …

This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over
the last 20 years, highlighting its success in solving complex optimization problems. We …

In this work, an approach is proposed to solve binary combinatorial problems using
continuous metaheuristics. It focuses on the importance of binarization in the optimization …

Power system planning and expansion start with forecasting the anticipated future load
requirement. Load forecasting is essential for the engineering perspective and a financial …

Chaotic maps are sources of randomness formed by a set of rules and chaotic variables.
They have been incorporated into metaheuristics because they improve the balance of …

Owing to the recent technological innovations, unmanned aerial vehicles (UAVs) are
progressively employed in various civil and military applications, including healthcare. This …

Abstract Design change planning is an inevitable part of the product development process.
Evolutionary algorithms (EAs) have been widely adopted to search for optimal change paths …

Currently, the industry is experiencing an exponential increase in dealing with binary-based
combinatorial problems. In this sense, metaheuristics have been a common trend in the field …