This document describes an intelligent water drop algorithm for optimization problems. It discusses how the algorithm is inspired by the natural behavior of water drops flowing in rivers. The algorithm uses virtual water drops that change the "soil" levels on graph edges to iteratively find optimal solutions. Pseudocode is provided to explain the algorithm's implementation and a flowchart shows the process. Comparisons are made to ant colony optimization algorithms and potential applications and future work are outlined.

1. UNDER THE GUIDENCE OF ABHISHEK PAUL
(ASSISTANT PROFESSOR,ECE DEPT.)

2. GROUP MEMBERS
 Illiya Manna (23000311015)
 Rajesh Biswas (23000311032)
 Sayantan Sarkar (23000311042)
 Sushmita Das (23000311050)
 Swatantra Saha (23000311051)
 Swayoni Bandopadhyay (23000311052)

3. CONTENTS
 INTRODUCTION
 NATURAL WATER DROPs
 IMPLEMENTATION OF IWD ALGORITHM
 IWD PSEUDOCODES
 FLOWCHART
 IWD vs ANT COLONY
 APPLICATIONS
 RELATED WORKS
 FUTURE ROADMAP
 CONCLUSION
 REFERENCE

4. WHAT IS INTELLIGENT WATER
DROP ALGORITHM?
 Intelligent Water Drops algorithm, or the IWD algorithm, is
a
Swarm based nature-inspired optimization algorithm.
 This algorithm contains a few essential elements of natural
water drops and actions and reactions that occur between
river's bed and the water drops that flow within.
 The IWD algorithm can be used for Combinatorial
optimization.

5. NATURAL WATER DROPS
 In nature, flowing water drops are observed mostly in rivers,
which form huge moving swarms. The paths that a natural
river follows have been created by a swarm of water drops.
For a swarm of water drops, the river in which they flow is
the part of the environment that has been dramatically
changed by the swarm and will also be changed in the
future.
 We are using the concept of the water path in the river. How
they prefers a path with less soil than a path with more soil.
The water drop prefers an easier path to a harder path when
it has to choose between several branches.

6. IMPLEMENTATION OF IWD ALGORITHMS
 The IWD algorithm employs a number
of IWDs to find the optimal solutions to a
given problem.
 The problem is represented by a graph
(N, E) with the node set N and edge set
E.
 This graph is the environment for the
IWDs and the IWDs flow on the edges of
the graph.
 Each IWD begins constructing its
solution gradually by traveling between
the nodes of the graph along the edges
until the IWD finally completes its
solution denoted by T IWD .
 Each solution T IWD is represented by
the edges that the IWD has visited.

7. THE IWD PSEUDOCODE
The pseudo-code of an IWD-based algorithm may be specified in
eight steps:
 Static parameter initialization
a) Problem representation in the form of a graph
b) Setting values for static parameters
 Dynamic parameter initialization: soil and velocity of IWDs
 Distribution of IWDs on the problem’s graph
 Solution construction by IWDs along with soil and velocity updating
a) Local soil updating on the graph
b) Soil and velocity updating on the IWDs
 Local search over each IWD’s solution (optional)
 Global soil updating
 Total-best solution updating
 Go to step 2 unless termination condition is satisfied

8. FLOWCHART

9. IWD vs ANT COLONY
 Every ant in an Ant Colony Optimization
(ACO) algorithm deposits pheromones on
each edge it visits. In contrast, an IWD
changes the amount of soil on edges.
 In the ACO algorithm, an ant cannot remove
pheromones from an edge whereas in the
IWD algorithm, an IWD can both remove
and add soil to an edge.
 Besides, the IWDs may gain different
velocities throughout an iteration of the IWD
algorithm whereas in ACO algorithms the
velocities of the ants are irrelevant.

10. RELATED WORKS
Scientists are beginning to realize more and more that nature is a great
source
for inspiration in order to develop intelligent systems and algorithms. In the
field of Computational Intelligence, especially Evolutionary Computation
and
Swarm-based systems, the degree of imitation from nature is surprisingly
high
and we are at the edge of developing and proposing new algorithms and/or
systems, which partially or fully follow nature and the actions and reactions
that happen in a specific natural system or species.

11. FUTURE SCOPE
We have planned to derive the stability
analysis of IWD and test them in various
functions. We are working along that line.
We will be applying the IWD equations in various
Control System techniques like Routh’s Array, Root
Locus and find out the stability. Then we are going to
derive the block diagram and transfer function. This will
be followed by implementing the transfer functions in
designing filter. Not only that, we are also planning to
implement them for mood detecting mechanisms.

12. CONCLUSIONS
Some properties that exist in natural water drops flowing in
rivers
are adopted in the algorithm for solving various optimization
problems. The proposed algorithm converges fast to optimum
solutions and finds good and promising results. This research
is in
the beginning stage of using water drops ideas to solve
engineering problems. So, there is much space to improve and
develop the IWD algorithm.

13. REFERENCE
 An approach to continuous optimization by the Intelligent Water Drops
algorithm Hamed Shah-Hosseini
 An Intelligent Water Drops Algorithm Based Service Selection And
Composition In Service Oriented Architecture By Palani Kumar D,
Gowsalya Elangovan, Rithu B, Anbuselven P
 Applications Of Intelligent Water Drops

14. THANK YOU