Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges
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REVIEW ARTICLE
Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges Janmenjoy Nayak1 · Bighnaraj Naik2 · Paidi Dinesh3 · Kanithi Vakula3 · Pandit Byomakesha Dash2 Received: 21 May 2020 / Accepted: 5 September 2020 © Springer Nature Singapore Pte Ltd 2020
Abstract Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA. Keywords Firefly algorithm · Swarm intelligence · Biomedical engineering and healthcare · Nature-inspired algorithm · Metaheuristics
I on behalf of the authors would like to state that the above manuscript is our original research work and it has not been published elsewhere. Also, it has not been submitted to any journal for publication. * Bighnaraj Naik [email protected] Janmenjoy Nayak [email protected] Paidi Dinesh [email protected] Kanithi Vakula [email protected] Pandit Byomakesha Dash [email protected] 1
Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), K Kotturu, Tekkali 532201, Andhra Pradesh, India
2
Department of Computer Application, Veer Surendra Sai University of Technology, Burla 768018, Odisha, India
3
Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam 532402, Andhra Pradesh, India
Abbreviations ABC Artificial Bee Colony AFA Adaptive Firefly Algorithm AIP Analogue image processing ANN Artificial Neural Network APSO Accelerated Particle Swarm Optimization BCFCM Bias Corrected Fuzzy C-Means BF Bilateral Filter BFA Binary Firefly Algorithm BME Bio Medical Engineering BPSO Binary Particle Swarm Optimization BSP Biomedical Signal Processing CDSS Clinical Decision Support System CFA Chaotic Firefly Algorithm CGA Controlled Genetic Algorithm CM Chinese Medicine CMFA Compa
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