Review Of Metaheuristics Research Articles

Revolutionizing sustainable supply chain management: A review of metaheuristics

This paper reviews the application of metaheuristics for optimized sustainable supply chain management (SSCM). This paper explores the potential of metaheuristics to improve the supply chain’s sustainability while enhancing its efficiency and competitiveness. The paper provides an overview of the principles of SSCM and the challenges businesses face in achieving sustainable supply chain management. It then introduces the concept of metaheuristics and describes their use in solving complex optimization problems. The paper reviews various metaheuristics algorithms applied to sustainable supply chain management and analyzes their effectiveness in addressing the challenges of SSCM. The paper also identifies the key factors that influence the success of using metaheuristics for SSCM, such as the choice of algorithm, problem complexity, and data quality. Finally, the paper provides recommendations for future research in this area and highlights the potential of metaheuristics to promote sustainable supply chain management. The review suggests that metaheuristics can be a valuable tool for optimizing sustainable supply chain management and improving supply chain operations’ sustainability, efficiency, and competitiveness.

A review of metaheuristics in robotics

Metaheuristics have a substantial history in fine-tuning machine learning algorithms. They gained tremendous popularity in many application domains. Robotics on the other hand is a wide research discipline that embraces artificial intelligence in a complex individually-thinking robot and distributed robots. Recently, metaheuristics made a significant impact on the application areas of collaborating robotics. This new trend of collaborating robotics, offers the possibility of enhanced task performance, high reliability, low unit complexity and decreased cost over traditional robotic systems. Collaborating robots however are more than just networks of independent agents; they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment. On the conceptual level, these bots can be empowered by the logics of metaheuristic algorithms which share the same functionalities and capabilities. This paper reviews the recent advances of metaheuristic algorithms on robotics applications. A taxonomy is provided as a reference for robotics designers.

Review of meta-heuristics and generalised evolutionary walk algorithm

Meta-heuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimisation problems. More than a dozen major meta-heuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrids of meta-heuristics. This paper intends to provide an overview of nature-inspired meta-heuristic algorithms, from a brief history to their applications. We try to analyse the main components of these algorithms and how and why they work. Then, we intend to provide a unified view of meta-heuristics by proposing a generalised evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.