Journal ArticleDOI
Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application
TLDR
A rigorous yet systematic review is presented to organize and summarize the information on the PSO algorithm and the developments and trends of its most basic as well as of some of the very notable implementations that have been introduced recently, bearing in mind the coverage of paradigm, theory, hybridization, parallelization, complex optimization, and the diverse applications of the algorithm.Abstract:
Over the ages, nature has constantly been a rich source of inspiration for science, with much still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial intelligence, was rendered to model the collective behavior of social swarms in nature. Ultimately, Particle Swarm Optimization algorithm (PSO) is arguably one of the most popular SI paradigms. Over the past two decades, PSO has been applied successfully, with good return as well, in a wide variety of fields of science and technology with a wider range of complex optimization problems, thereby occupying a prominent position in the optimization field. However, through in-depth studies, a number of problems with the algorithm have been detected and identified; e.g., issues regarding convergence, diversity, and stability. Consequently, since its birth in the mid-1990s, PSO has witnessed a myriad of enhancements, extensions, and variants in various aspects of the algorithm, specifically after the twentieth century, and the related research has therefore now reached an impressive state. In this paper, a rigorous yet systematic review is presented to organize and summarize the information on the PSO algorithm and the developments and trends of its most basic as well as of some of the very notable implementations that have been introduced recently, bearing in mind the coverage of paradigm, theory, hybridization, parallelization, complex optimization, and the diverse applications of the algorithm, making it more accessible. Ease for researchers to determine which PSO variant is currently best suited or to be invented for a given optimization problem or application. This up-to-date review also highlights the current pressing issues and intriguing open challenges haunting PSO, prompting scholars and researchers to conduct further research both on the theory and application of the algorithm in the forthcoming years.read more
Citations
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Journal ArticleDOI
Beluga whale optimization: A novel nature-inspired metaheuristic algorithm
TL;DR: Zhang et al. as discussed by the authors proposed a swarm-based metaheuristic algorithm inspired from the behaviors of beluga whales, called Beluga Whale Optimization (BWO), to solve optimization problem.
Journal ArticleDOI
Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review
TL;DR: The Particle Swarm Optimization (PSO) algorithm has been widely used in the field of Artificial Intelligence (AI) and has been applied in many real-world applications, such as health care, environmental, industrial, commercial, and smart city as mentioned in this paper .
Journal ArticleDOI
An Overview of Variants and Advancements of PSO Algorithm
TL;DR: An overview of the PSO algorithm is presented, the basic concepts and parameters of PSO are explained, and various advances in relation to PSO, including its modifications, extensions, hybridization, theoretical analysis, are included.
Posted Content
GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles
TL;DR: In this article, a mathematically optimal model with two objective functions is developed to analyze the relationship between upfront investments and operating costs and service coverage of charging station system and it was solved by Particle Swarm Optimization.
Journal ArticleDOI
Emerging Trends in Blockchain Technology and Applications: A Review and Outlook
TL;DR: In this article , a state-of-the-art review is conducted on the most influential articles, conference papers, and review papers related to Blockchain published from 2013 to 2020 and indexed by the Web of Science Core CollectionTM (WoS) world's literature database.
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