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'''Intelligent water drops''' ('''IWD''') '''algorithm'''<ref name=shah-hosseini2007>
'''Intelligent water drops''' ('''IWD''') '''algorithm'''<ref name=shah-hosseini2009/> 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 may fall into the category of [[swarm intelligence]] and [[metaheuristic]]. Intrinsically, the IWD algorithm can be used for [[combinatorial optimization]]. However, it may be adapted for continuous optimization too. The IWD was first introduced for the [[traveling salesman problem]] in 2007.<ref name=shah-hosseini2007/> Since then, multitude of researchers have focused on improving the algorithm for different problems.
{{cite journal
|last=Shah-Hosseini
|first=H.
|title=Problem solving by intelligent water drops
|work=Proceedings of the IEEE Congress on Evolutionary Computation
|pages=3226–3231
|year=2007
|doi=10.1109/CEC.2007.4424885
}}</ref><ref name=shah-hosseini2009>
{{cite journal
|title=The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm
|author=Shah-Hosseini, H.
|journal=International Journal of Bio-Inspired Computation
|volume=1
|issue=1/2
|pages=71–79
|year=2009
|doi=10.1504/ijbic.2009.022775
}}
</ref> 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 was first introduced for the [[traveling salesman problem]] in 2007.


==Introduction==
Almost every IWD algorithm is composed of two parts: a graph that plays the role of distributed memory on which soils of different edges are preserved, and the moving part of the IWD algorithm, which is a few number of Intelligent water drops. These intelligent water drops (IWDs) both compete and cooperate to find better solutions and by changing soils of the graph, the paths to better solutions become more reachable. It is mentioned that the IWD-based algorithms need at least two IWDs to work.
Almost every IWD algorithm is composed of two parts: a graph that plays the role of distributed memory on which soils of different edges are preserved, and the moving part of the IWD algorithm, which is a few number of Intelligent water drops. These intelligent water drops (IWDs) both compete and cooperate to find better solutions and by changing soils of the graph, the paths to better solutions become more reachable. It is mentioned that the IWD-based algorithms need at least two IWDs to work.


==Pseudo-code==
The IWD algorithm has two types of parameters: static and dynamic parameters. Static parameters are constant during the process of the IWD algorithm. Dynamic parameters are reinitialized after each iteration of the IWD algorithm. The pseudo-code of an IWD-based algorithm may be specified in eight steps:
The IWD algorithm has two types of parameters: static and dynamic parameters. Static parameters are constant during the process of the IWD algorithm. Dynamic parameters are reinitialized after each iteration of the IWD algorithm. The pseudo-code of an IWD-based algorithm may be specified in eight steps:


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==References==
==References==
{{reflist|2|refs=
{{reflist|2}}

<ref name=shah-hosseini2007>
{{cite journal
|last=Shah-Hosseini
|first=H.
|title=Problem solving by intelligent water drops
|work=Proceedings of the IEEE Congress on Evolutionary Computation
|pages=3226–3231
|year=2007
|doi=10.1109/CEC.2007.4424885
}}</ref>

<ref name=shah-hosseini2009>
{{cite journal
|title=The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm
|author=Shah-Hosseini, H.
|journal=International Journal of Bio-Inspired Computation
|volume=1
|issue=1/2
|pages=71–79
|year=2009
|doi=10.1504/ijbic.2009.022775
}}
</ref>
}}


[[Category:Nature-inspired metaheuristics]]
[[Category:Nature-inspired metaheuristics]]

Revision as of 13:07, 12 August 2016

Intelligent water drops (IWD) algorithm[1][2] 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 was first introduced for the traveling salesman problem in 2007.

Almost every IWD algorithm is composed of two parts: a graph that plays the role of distributed memory on which soils of different edges are preserved, and the moving part of the IWD algorithm, which is a few number of Intelligent water drops. These intelligent water drops (IWDs) both compete and cooperate to find better solutions and by changing soils of the graph, the paths to better solutions become more reachable. It is mentioned that the IWD-based algorithms need at least two IWDs to work.

The IWD algorithm has two types of parameters: static and dynamic parameters. Static parameters are constant during the process of the IWD algorithm. Dynamic parameters are reinitialized after each iteration of the IWD algorithm. The pseudo-code of an IWD-based algorithm may be specified in eight steps:

1) Static parameter initialization
a) Problem representation in the form of a graph
b) Setting values for static parameters
2) Dynamic parameter initialization: soil and velocity of IWDs
3) Distribution of IWDs on the problem’s graph
4) 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
5) Local search over each IWD’s solution (optional)
6) Global soil updating
7) Total-best solution updating
8) Go to step 2 unless termination condition is satisfied

References

  1. ^ Shah-Hosseini, H. (2007). "Problem solving by intelligent water drops". Proceedings of the IEEE Congress on Evolutionary Computation: 3226–3231. doi:10.1109/CEC.2007.4424885.
  2. ^ Shah-Hosseini, H. (2009). "The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm". International Journal of Bio-Inspired Computation. 1 (1/2): 71–79. doi:10.1504/ijbic.2009.022775.