OPTIMIZATION OF SENSOR COVERAGE IN HYBRID POSITIONING SYSTEMS
The paper considers the problem of optimizing the placement of sensors in hybrid positioning systems operating in dynamic production environments.
The paper considers the problem of optimizing the placement of sensors in hybrid positioning systems operating in dynamic production environments.
The presented study is dedicated to the dynamic pathfinding problem for UV. Since the automation of UV movement is an important area in many applied domains like robotics, the development of drones, autopilots, and self-learnable platforms, we propose and study a promising approach based on the algorithm of swarm AI. Given the 2D environment with multiple obstacles of rectangular shape, the task is to dynamically calculate a suboptimal path from the starting point to the target.
In this paper, we are interested in the Probabilistic Traveling Salesman Problem with Deadlines (PTSPD) where clients must be contacted, in addition to their random availability before a set deadline. The main objective is to find an optimal route that covers a random subset of visitors in the same order as they appear on the tour, attempting to keep the path as short as possible. This problem is regarded as being $\sharp$P-hard. Ant Colony Optimization (ACO) has been frequently employed to resolve this challenging optimization problem. However, we suggest an enhanc
The demand for efficient solutions to optimization problems with uncertain and stochastic data is increasing. Probabilistic traveling salesman problem (PTSP) is a class of Stochastic Combinatorial Optimization Problems (SCOPs) involving partially unknown information about problem data with a known probability distribution. It consists to minimize the expected length of the tour where each customer requires a visit only with a given probability, at which customers who do not need a tour are just ignored without further optimization. Since the PTSP is NP-hard, the usage of metaheuristic me
Simulated annealing algorithm is one of the most popular metaheuristics that has been successfully applied to many optimization problems. The main advantage of SA is its ability to escape from local optima by allowing hill-climbing moves and exploring new solutions at the beginning of the search process. One of its drawbacks is its slow convergence, requiring high computational time with a good set of parameter values to find a reasonable solution. In this work, a new improved SA is proposed to solve the well-known travelling salesman problem. In order to improve SA