Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges.

Area of Science:

• Computational Intelligence
• Swarm Intelligence
• Metaheuristic Optimization

Background:

• Nature-inspired optimization algorithms (NIOA) are widely used for complex problem-solving.
• The Firefly Algorithm (FA) is a prominent swarm intelligence (SI) metaheuristic known for its efficiency.

Purpose of the Study:

• To provide an in-depth analysis of Firefly Algorithm (FA) variants, their importance, applications, and enhancements.
• To highlight the significant impact of FA in biomedical engineering (BME) and healthcare (HC) research.
• To motivate further innovation in BME and HC problem-solving using FA.

Main Methods:

• Comprehensive literature review of FA and its variants.
• Analysis of FA's application in biomedical engineering and healthcare domains.
• Identification of current trends and future research directions for FA in BME and HC.

Main Results:

• FA has a proven track record in addressing complex challenges within BME and HC.
• Numerous variants and enhancements of FA have been developed, expanding its applicability.
• FA demonstrates significant potential for novel solutions in healthcare and biomedical engineering.

Conclusions:

• The Firefly Algorithm is a powerful and versatile tool for BME and HC research.
• Continued research into FA variants and applications can lead to breakthroughs in healthcare.
• This review serves as a foundation for future advancements using FA in these critical fields.