Eva Tuba

Drone Placement for Optimal Coverage by Brain Storm Optimization Algorithm

Unmanned aerial vehicles or drones are used in wide range of applications and one of them is area monitoring. Finding the optimal positions for drones so that the coverage is maximized, while reducing the fuel consumption represents computationally hard problem. For these kinds of problems, swarm intelligence algorithms have been successfully used. In this paper we propose recent brain storm optimization algorithm for finding the locations for static drones. Optimal drone placement maximizes the number of covered targets while minimizing drones altitude. The proposed method was tested in two different environments, with uniformly and clustered deployed targets. Based on the obtained results it can be concluded that brain storm optimization is appropriate for solving drone placement problem in both considered environments.

Adjusted Artificial Bee Colony Algorithm for the Minimum Weight Triangulation

The minimum weight triangulation is a well-known NP-hard problem often used for the construction of triangulated random network models of land contours. Since it is an intractable problem, the required computational time for an exhaustive search algorithm grows exponentially with the number of points in 2D space. Nature-inspired swarm intelligence algorithms are prominent and efficient optimization techniques for solving that kind of problems. In this paper, we adjusted the artificial bee colony algorithm for the minimum weight triangulation problem. Our adjusted algorithm has been implemented and tested on several randomly generated instances of points in the plane. The performance of our proposed method was compared to the performance of other stochastic optimization algorithms, as well as with the exhaustive search for smaller instances. The simulation results show that our proposed algorithm in almost all cases outperforms other compared algorithms.

Web Intelligence Data Clustering by Bare Bone Fireworks Algorithm Combined with K-Means

Data mining and clustering are important elements of various applications in different fields. One of the areas were clustering is rather frequently used is web intelligence, which nowadays represents an important research area. Data collected from the web are usually very complex, dynamic, without structure and rather large. Traditional clustering techniques are not efficient enough and need to be improved. In this paper, we propose combination of recent swarm intelligence algorithm, bare bones fireworks algorithm, and k-means for clustering web intelligence data. The proposed method was compared with other approaches from literature. Based on the experimental results, it can be concluded that the proposed method has very promising characteristics in terms of the quality of clustering, as well as the execution time.

Bare Bones Fireworks Algorithm for Capacitated p-Median Problem.

The p-median problem represents a widely applicable problem in different fields such as operational research and supply chain management. Numerous versions of the p-median problem are defined in literature and it has been shown that it belongs to the class of NP-hard problems. In this paper a recent swarm intelligence algorithm, the bare bones fireworks algorithm, which is the latest version of the fireworks algorithm is proposed for solving capacitated p-median problem. The proposed method is tested on benchmark datasets with different values for p. Performance of the proposed method was compared to other methods from literature and it exhibited competitive results with possibility for further improvements.