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Firebug Swarm Optimization Algorithm: An Overview and Applications
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AI
DOI：10.3390/signals7010008.png)
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Abstract
En 中文
This survey delves into the Firebug Swarm Optimization (FSO) algorithm, an advanced global optimization algorithm that plays a pivotal role in modern swarm intelligence optimization techniques. It explores the core principles of the FSO algorithm and examines the various hybrid variants developed to address complex optimization challenges. This survey also traces the evolution of swarm optimization methods, shedding light onto the natural phenomena and biological processes that have inspired these algorithms. Furthermore, it highlights the diverse real-world applications of the FSO algorithm, showcasing its effectiveness in fields such as engineering, data science, and artificial intelligence. To provide a comprehensive comparison, the survey includes a case study that evaluates the FSO algorithm's performance against other existing algorithms. Lastly, the survey identifies key open research questions and suggests potential future directions for advancing the FSO algorithm and other nature-inspired optimization techniques, aiming to overcome current limitations and unlock new possibilities.
Keywords:
optimization
meta-heuristic
artificial intelligent
swarm-based algorithms
firebug swarm optimization
clustering
nature-inspired algorithms
wireless sensor networks
cognitive radio
photovoltaic systems
Journal
S
IF:
2.6
Papers:
70
Citations:
392
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Cited Papers
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