Abstract
In the optimization areas, there are different algorithms that have been applied such as swarm intelligence algorithms. The researchers have found different algorithms by simulating the behaviors of various swarms of insects and animals such as fishes, bees, and ants. The intelligent water drops algorithm is one of the recently developed algorithms in the swarm intelligence field; this algorithm mimicked the dynamic of river systems. The natural water drops used to develop Intelligent Water Drop (IWD) algorithm. Therefore, the mechanisms that happen in rivers have inspired the researchers mainly to create new algorithms. IWD is a population-based algorithm where each drop represents a solution and the sharing between the drops during the search lead to a better drops (or solutions). This paper presents recent developments of the IWD algorithms in terms of theory and application. This paper concludes many of research directions that are necessary for the future of IWD algorithm.
Highlights
Over the last two decades, the computational researchers had fascinated in natural sciences as a basis of modeling
It presents possibility for modifications in the standard algorithm, incorporating other mechanisms that are present in natural rivers and/or inventing local heuristics that are better fitted with the Intelligent Water Drops (IWD) algorithm
The study of Shah-Hosseini [6] had modified IWDA to be applied to automatic multi-level thresholding, calling it IWDAMLT which finds the number of thresholds and their values automatically based on a modified Otsu‟s value
Summary
Over the last two decades, the computational researchers had fascinated in natural sciences as a basis of modeling. The reason behind the success is partly due to the grounding benefit of IWD in comparison to other conventional optimization techniques [4, 6, and 7]. It encompasses a less sophisticated and yet comprehendible mathematical model. It is adaptable to numerous optimization problems which are relevant to both discrete and continuous problems, quickly converging it to the optimal solution. It functions in structuring the solution of population based on data obtained by experience from iteration of the search rather than taking into consideration the refinement of the present population.
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