Abstract
In this paper, a new optimization algorithm called Motion-Encoded Genetic Algorithm with Multiple Parents (MEGA-MPC) is developed to locate moving targets using multiple Unmanned Aerial Vehicles (UAVs). Bayesian theory is used to formulate the moving target tracking as an optimization problem where target detection probability defines the objective function as the probability of detecting the target. In the developed MEGA-MPC algorithm, a series of UAV motion paths encodes the search trajectory. In every iteration of the MEGA-MPC algorithm, UAV motion paths undergo evolution. The proposed approach for dynamic target search using multi-UAVs uses parallel computations to solve the optimization problem based on the MEGA-MPC algorithm where Each UAV can communicate with other UAVs if requested. The algorithm's performance is tested with various characteristics under six distinct scenarios using a different number of UAVs and targets. The statistical analysis of the results obtained using MEGA-MPC compared with other well-known metaheuristics shows that MEGA-MPC offers better solutions to find dynamic targets since it outperforms all the compared algorithms.