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CHAPTER

KIDNEY-INSPIRED ALGORITHM

AND FUZZY CLUSTERING FOR 11

BIOMEDICAL DATA ANALYSIS

† †
Janmenjoy Nayak*, Kanithi Vakula*, Pandit Byomakesha Dash , Bighnaraj Naik
Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, India* Department of
Computer Application, Veer Surendra Sai University of Technology, Burla, India †

11.1 INTRODUCTION
Optimization is generally a numerical problem found in all engineering regulations as well as various
other fields of data mining. Precisely, optimization means finding the finest desirable solution to a
problem. Problems of optimization are various and abundant. Therefore, techniques for resolving these
problems were necessary. Previous techniques to resolve optimization problems necessitate vast com-
putational endeavors, which tend to be unsuccessful as the problem range rises. This is the inspiration
for utilizing bio-inspired optimization algorithms [1–23] as computationally well-organized alterna-
tives to deterministic advances. The real attractiveness of nature-inspired algorithms lies in the fact
that it gets their solitary stimulation from nature. Nowadays, researchers are paying more attention
to nature-inspired optimization algorithms as these are proficient in resolving difficult problems such
as constrained as well as unconstrained type problems. In these modern times, the researchers are
attempting to replicate nature in expertise due to the fact that nature is known to be the greatest instruc-
tor for technology. Nature-inspired optimization algorithms are procured from the behavior of physical
or biological processes in the natural world.
A new era known as bio inspired in computing encircles a broad range of applications by covering
nearly all areas including security, computer networks, robotics, control systems, data mining, produc-
tion engineering, biomedical engineering, and many more. Bio Inspired algorithms (BIAs) duplicate or
mimic the approach of nature from the time when many biological procedures were thought of as pro-
cedures of controlled optimization. Inventing a plan for bio inspired algorithms engages selecting an
appropriate illustration of difficulty, estimating the solution using a robustness function, and defining
operators so as to fabricate a novel set of clarifications. An enormous collection of literature exists on
bio inspired advances for resolving an inspiring collection of tribulations. Moreover, many new studies
have commented on the achievement of methods for explaining tricky problems in all the main fields of
computer science. The two most primary and victorious instructions in BIAs involve swarm-based and
evolutionary algorithms, which are stimulated by the natural development and communal behavior in
animals correspondingly. From the previous few decades, the rise in difficulty of real-life problems has
increased the need of enhanced metaheuristic techniques. A metaheuristic is an upper rank heuristic

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