Dragonfly Algorithm Strategy Parameters Analysis on Swarm Robot Multi-Target Search Efficiency

Dragonfly Algorithm (DA) is a Swarm Intelligence (SI) based optimization strategy. Since its development in 2016, DA has been widely utilized in many technology and engineering applications. Target searching by the swarm robots (SR) is one of the applications that take advantage of the DA strategy advancement. Multi-target search task is the latest inquiry in the target search problem domain whereby the objective is to search all available targets within the minimum time possible. To achieve this purpose, the DA parameters need to be optimized to ensure the search outcome is in the optimum and efficient condition. This article presented the DA parameters analysis within the scope of multi-target search problems based on the swarm robots. There is a total of six parameters included in this analysis which are inertia weight ($\omega$), alignment weight (a), cohesion weight (c), separation weight (s), food attraction factor (f), and enemy distraction factor (e). The analysis results are presented in detail with a focus on the trends and effects of each parameter on the search efficiency outcomes with the least iteration (most efficient) recorded of 19.9 iterations. The results data can be benchmarks or references not only in the multi-target search problem but also in other research domains that are based on the DA strategy.