-Proportional, Integral and Derivative (PID) controllers are the most popular type of controller used in industrial applications because of their notable simplicity and effective implementation. However, manual tuning of these controllers is tedious and often leads to poor performance. The conventional Ziegler-Nichols (Z-N) method of PID tuning was done experimentally enables easy identification stable PID parameters in a short time, but is accompanied by overshoot, high steady-state error, and large rise time. Therefore, in this study, the modern heuristics approach of Particle Swarm Optimization (PSO) was employed to enhance the capabilities of the conventional Z-N technique. PSO with the constriction coefficient method experimentally demonstrated the ability to efficiently and effectively identify optimal PID controller parameters for attitude stabilization of a quadrotor.

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In this paper, a new optimization algorithm called motion-encoded electric charged particles optimization (ECPO-ME) is developed to find moving targets using unmanned aerial vehicles (UAV). The algorithm is based on the combination of the ECPO (i.e., the base algorithm) with the ME mechanism. This study is directly applicable to a real-world scenario, for instance the movement of a misplaced animal can be detected and subsequently its location can be transmitted to its caretaker. Using Bayesian theory, finding the location of a moving target is formulated as an optimization problem wherein the objective function is to maximize the probability of detecting the target. In the proposed ECPO-ME algorithm, the search trajectory is encoded as a series of UAV motion paths. These paths evolve in each iteration of the ECPO-ME algorithm. The performance of the algorithm is tested for six different scenarios with different characteristics. A statistical analysis is carried out to compare the results obtained from ECPO-ME with other well-known metaheuristics, widely used for benchmarking studies. The results found show that the ECPO-ME has great potential in finding moving targets, since it outperforms the base algorithm (i.e., ECPO) by as much as 2.16%, 5.26%, 7.17%, 14.72%, 0.79% and 3.38% for the investigated scenarios, respectively.

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. INDEX TERMSDynamic target search, motion-encoded genetic algorithm, probabilistic targets, unmanned aerial vehicles I. INTRODUCTION VOLUME x, 20xx