review · Archives of Computational Methods in Engineering
This systematic review examines the Particle Swarm Optimization (PSO) algorithm, a popular Swarm Intelligence technique inspired by collective natural behaviour. It surveys research published between 2017 and 2019, focusing on algorithmic methods such as hybridisation, improvements, and variants. The review also categorises PSO's real-world applications across diverse domains including healthcare, environmental management, industrial processes, commercial uses, and smart city initiatives. Technical characteristics like accuracy and evaluation environments are considered to assess the effectiveness of different PSO approaches. The paper discusses the strengths and weaknesses of existing studies, identifying open issues and suggesting future research directions for the algorithm.
Understanding the evolution and diverse applications of Particle Swarm Optimization is crucial for researchers and practitioners. This review provides a comprehensive overview of recent developments, helping to identify effective methods and potential areas for further innovation in optimisation problems across various fields.
This systematic review highlights the broad applicability of Particle Swarm Optimization across sectors like healthcare, industry, commerce, and smart cities. It identifies existing real-world applications and could inform organisations seeking efficient optimisation solutions for complex problems. The work surveys applied research, indicating that PSO is already being utilised in various practical contexts.
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Abstract Throughout the centuries, nature has been a source of inspiration, with much still to learn from and discover about. Among many others, Swarm Intelligence (SI), a substantial branch of Artificial Intelligence, is built on the intelligent collective behavior of social swarms in nature. One of the most popular SI paradigms, the Particle Swarm Optimization algorithm (PSO), is presented in this work. Many changes have been made to PSO since its inception in the mid 1990s. Since their learning about the technique, researchers and practitioners have developed new applications, derived new versions, and published theoretical studies on the potential influence of various parameters and aspects of the algorithm. Various perspectives are surveyed in this paper on existing and ongoing research, including algorithm methods, diverse application domains, open issues, and future perspectives, based on the Systematic Review (SR) process. More specifically, this paper analyzes the existing research on methods and applications published between 2017 and 2019 in a technical taxonomy of the picked content, including hybridization, improvement, and variants of PSO, as well as real-world applications of the algorithm categorized into: health-care, environmental, industrial, commercial, smart city, and general aspects applications. Some technical characteristics, including accuracy, evaluation environments, and proposed case study are involved to investigate the effectiveness of different PSO methods and applications. Each addressed study has some valuable advantages and unavoidable drawbacks which are discussed and has accordingly yielded some hints presented for addressing the weaknesses of those studies and highlighting the open issues and future research perspectives on the algorithm.
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DOI: 10.1007/s11831-021-09694-4
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