Nowadays most busy international airports and their corresponding terminal areas are suffering from huge congestion issues due to the simultaneity of their arrival aircraft. The aim of this paper is to establish a new separation method using time- based-separation, speed modification during approach phases and Point Merge System (PMS) so as to ensure efficiently the traffic flow. This work took as a case study the busiest airport of Morocco, The Mohammed V International airport of Casablanca. The proposed management model offers very good results when compared with other models such as the first-come first-served (FCFS) model.

Optimization of hyperparameters of artificial neural network (ANN) usually involves a trial and error approach which is not only computationally expensive but also fails to predict a near-optimal solution most of the time. To design a better optimized ANN model, evolutionary algorithms are widely utilized to determine hyperparameters. This work proposes hyperparameters optimization of the ANN model using an improved particle swarm optimization (IPSO) algorithm. The different ANN hyperparameters considered are a number of hidden layers, neurons in each hidden layer, activation function, and training function. The proposed technique is validated using inverse modeling of two meander line electromagnetic bandgap unit cells and a slotted ultra-wideband antenna loaded with EBG structures. Three other evolutionary algorithms viz. hybrid PSO, conventional PSO, and genetic algorithm are also adopted for the hyperparameter optimization of the ANN models for comparative analysis. Performances of all the models are evaluated using quantitative assessment parameters viz. mean square error, mean absolute percentage deviation, and coefficient of determination (R2). The comparative investigation establishes the accurate and efficient prediction capability of the ANN models tuned using IPSO compared to other evolutionary algorithms.

Inverted pendulum systems (IPSs) are mostly used to demonstrate the control rules for keeping the pendulum at a balanced upright position due to a slight force applied to the cart system. This paper presents an application for nonlinear control of an x-z type IPS by using a proportional-integral-derivative (PID) controller with newly established evolutionary tuning method Lightning Search Algorithm (LSA). A single, double and triple PID controller system is tested with the conventional and the self-tuning controllers to get a better understanding of the performance of the given system. The mathematical modelling of the x-z type IPS, the theoretical explanation of the LSA and the simulation analysis of the x-z type IPS is put forward entirely. The LSA algorithm solves the optimization problem of PID controller in an evolutionary way. The most effective way of making comparisons is evaluating the performance results with a well-known optimization technique or with the previous accepted results. We have compared the system’s performance with particle swarm optimization and with a classical control study in the literature. According to the simulation results, LSA-tuned PID controller has the ability to decrease the overshoot better than the conventional-tuned controllers. Finally, it can be concluded that the LSA-supported PID can better stabilize the pendulum angle and track the reference better for non-disturbed and the slightly disturbed systems.

In this article, we improve the efficiency of a turbine blade inspection robotic workcell. The workcell consists of a stationary camera and a 6-axis serial robot that is holding a blade and presenting different zones of the blade to the camera for inspection. The problem at hand consists of a 6-DOF (degree of freedom) continuous optimization of the camera placement and a discrete combinatorial optimization of the sequence of inspection poses (images). For each image, all robot configurations (up to eight) are taken into consideration. A novel combined approach is introduced, called blind dynamic particle swarm optimization (BD-PSO), to simultaneously obtain the optimal design for both domains. The objective is to minimize the cycle time of the inspection process, while avoiding any collisions. Even though PSO is vastly used in engineering problems, the novelty of our combinatorial optimization method is in its ability to be used efficiently in traveling salesman problems where the distances between the cities are unknown and subject to change. This highly unpredictable environment is the case of the inspection cell where the cycle time between two images will change for different camera placements.

In this paper, the author proposes a method based on two recent evolutionary algorithms (EAS): particle swarm optimization (PSO) and differential evolution (DE) to design nonuniformly placed linear arrays of half-wavelength long dipoles. The objective of the work is to generate pencil beam in horizontal (for parallel array) and vertical (for collinear array) plane with minimum standing wave ratio (SWR) and fixed side lobe level (SLL). Dynamic range ratio (DRR) of current amplitude distribution is kept at a fixed value. Two different examples have been presented having different array alignments. For both the configurations parallel and collinear, the excitation distribution and geometry of individual array elements are perturbed to accomplish the designing goal. Coupling effect between the elements is analyzed using induced electromotive force (EMF) method and minimized in terms of SWR. Numerical results obtained from both the algorithms are statistically compared to present a comprehensive overview. Beside this, the article also efficiently computes the trade-off curves between SLL, beam width, and number of array elements for nonuniformly spaced parallel array. It featured the average element spacing versus SWR curve for nonuniformly separated arrays. Furthermore, minimum achievable SLL performances of uniformly and nonuniformly spaced parallel arrays are compared for same average spacing in the proposed work.

In this paper, a method for design and optimization of folded reflect-array antennas is proposed based on particleswarm optimization (PSO). In addition to such a powerful optimization algorithm, two further requirements have to be fulfilled. The first one is a good and fast algorithm for the exact prognosis of the far-field radiation diagram, resulting from a specific element configuration on the reflector. Additionally, a good choice of the fitness function for the evaluation of the resulting radiation diagrams is necessary. In both, reflect-array-related aspects such as phase truncation, reflection losses, and cell discontinuities have to be considered. Antenna optimization based on this technique is presented in this paper at the example of two 77 GHz folded reflect-array antennas. The efficiency of this approach is demonstrated with these examples, and the results are verified by measurement, showing an excellent agreement with the specifications of the diagram masks. The implemented tool, including a realistic antenna diagram preview, allows the investigation of the design parameters’ influence on the antenna performance, such as illumination amplitude, high substrate losses, and phase truncation.

The measurements of physical level parameters can become the area where decisions about cognitive radio will have the most striking effect. Field-programmable gate array (FPGA) enables real-time analyses of physical layer data to satisfy constraints like dynamic spectrum allocations, data throughput, and the coding rate. Cognitive radio will be based on simple network management techniques, using remote procedure calls. Intelligent knowledge-base system (IKBS) techniques will be used to search the parameter space in selecting changes to the system. Worldwide Interoperability for Microwave Access (WiMAX) PHY-layer functions will be managed cognitively by a FPGA-based controller to optimize the performance of the system. Instead of simple bit loading methods, the global multi-criteria optimization promise possibility to adapt more parameters with respect to several objectives. In this paper, the application of particle swarm optimization to fixed WiMAX-OFDM (Orthogonal Frequency Division Multiplexing) parameter adaptation is presented and compared with the greedy bit loading algorithm.