Intelligent water drops algorithm
Intelligent water drops (IWD) algorithm[1] 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.[2] Since then, multitude of researchers have focused on improving the algorithm for different problems.
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.
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:
- 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
- ^ 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.
- ^ 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.