{"title":"Quantum-inspired metaheuristic algorithms for Industry 4.0: A scientometric analysis","authors":"Pooja, Sandeep Kumar Sood","doi":"10.1016/j.engappai.2024.109635","DOIUrl":null,"url":null,"abstract":"<div><div>Quantum-inspired Metaheuristic algorithms have redefined non-deterministic polynomial time hard optimization challenges by leveraging quantum mechanics principles. These algorithms herald a broad range of application scenarios in Industry 4.0 and offer feasible time solutions for complex, large-scale industrial landscapes. The potential benefits provided by the quantum-inspired metaheuristic algorithms have accelerated the scientific advancements in this domain. Consequently, the present research contributes to the existing knowledge base by presenting the intellectual landscape through scientometric and systematic literature analysis. The study is conducted on the dataset derived from the Scopus and Web of Science databases, covering 2001 to 2023. The study employs co-citation and co-occurrence analyses to discern prominent research topics, emerging research frontiers, significant authors, and the most collaborating countries. The research findings underscore that electric vehicles, energy efficiency, and combinatorial optimization are prominent research topics, while carbon emission, resource management, and path planning are burgeoning areas of exploration in this knowledge domain. The intricate and entangled network linkage determines that the research community in this domain fosters a dynamic and synergistic relationship. Overall, the pivotal insights and the research challenges articulated in this article offer valuable insights to researchers and the academic community, aiding in discerning the intellectual terrain and emerging research patterns in quantum-inspired metaheuristic algorithms. This, in turn, fosters the advancement of innovation and facilitates well-informed decision-making within this evolving research paradigm.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109635"},"PeriodicalIF":7.5000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624017937","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Quantum-inspired Metaheuristic algorithms have redefined non-deterministic polynomial time hard optimization challenges by leveraging quantum mechanics principles. These algorithms herald a broad range of application scenarios in Industry 4.0 and offer feasible time solutions for complex, large-scale industrial landscapes. The potential benefits provided by the quantum-inspired metaheuristic algorithms have accelerated the scientific advancements in this domain. Consequently, the present research contributes to the existing knowledge base by presenting the intellectual landscape through scientometric and systematic literature analysis. The study is conducted on the dataset derived from the Scopus and Web of Science databases, covering 2001 to 2023. The study employs co-citation and co-occurrence analyses to discern prominent research topics, emerging research frontiers, significant authors, and the most collaborating countries. The research findings underscore that electric vehicles, energy efficiency, and combinatorial optimization are prominent research topics, while carbon emission, resource management, and path planning are burgeoning areas of exploration in this knowledge domain. The intricate and entangled network linkage determines that the research community in this domain fosters a dynamic and synergistic relationship. Overall, the pivotal insights and the research challenges articulated in this article offer valuable insights to researchers and the academic community, aiding in discerning the intellectual terrain and emerging research patterns in quantum-inspired metaheuristic algorithms. This, in turn, fosters the advancement of innovation and facilitates well-informed decision-making within this evolving research paradigm.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.