The indoor wireless communication in general, suffers from several challenges like, signal reflection, diffraction, and attenuation. With these problems, the error range is increased significantly and the accuracy will be lost. To address those problems, Mini Zone (MZ)e technique propos in this paper which aim to partition building into small areas lead to more simplicity and flexibility to assign suitable parameters for specific area rather than whole building. To do that, case study building separated to seven zone (A-G). Each zone has its specific characteristics related to its contents such as, objects, walls, windows and any types of materials in addition to the distance between transmitters and each zone. We took in account these specific parameters to estimate the correct position. 56 receivers (8 for each zone) and 3 transmitters deployed in the case study building. The Wireless Insite Package has been used to design the chosen building and measure the required parameters. The target position has been estimated depending on RSS and ToA methods The objectives of this study are to implement a dynamic system that has capabilities to estimate position under deference conditions like LOS or NLO with the same accuracy. In addition, study the suitability of TOA and RSS methods to estimate position. These objectives were done based on the proposed technique by decrease error in the whole system to an acceptable level to be (0.293502m). Also, the results confirm that the TOA method was better than RSS by using propos technique.

Grey wolf optimizer (GWO) is a nature inspired optimization algorithm. It can be used to solve both minimization and maximization problems. The binary version of GWO (BGWO) uses binary values for wolves’ positions rather than probabilistic values in the original GWO. Integrating BGWO with quantum inspired operations produce a novel enhanced quantum inspired binary grey wolf algorithm (EQI-BGWO). In this paper we used feature selection as an optimization problem to evaluate the performance of our proposed algorithm EQI-BGWO. Our method was evaluated against BGWO method by comparing the fitness value, number of eliminated features and global optima iteration number. it showed a better accuracy and eliminates higher number of features with good performance. Results show that the average error rate enhanced from 0.09 to 0.06 and from 0.53 to 0.52 and from 0.26 to 0.23 for zoo, Lymphography and diabetes dataset respectively using EQI-BGWO, Where the average number of eliminated features was reduced from 6.6 to 6.7 for zoo dataset and from 7.3 to 7.1 for Lymphography dataset and from 2.9 to 3.2 for diabetes dataset.

This paper presents modified salp swarm algorithm (MSSA) for solution of power system scheduling problems with diverse complexity level. Salp swarm algorithm (SSA) is a recently proposed efficient nature inspired (NI) optimization method inspired by foraging behaviour of salps found in deep ocean. SSA sometimes suffers to stagnation at local minima, to overcome this problem and enhancing searching capability by both exploration and exploitation MSSA is proposed in this paper. MSSA applied and tested on two types of problems. Type one is having five benchmark functions of diverse nature, whereas type two is related with real world problem of power system scheduling of a standard IEEE 114 bus system with 54 thermal units for (i) single area system, (ii) two area system and (iii) three area system. Finally Outcome of simulation results are validated with reported results by other method available in literature.

This paper proposes a collision-free path planning algorithm based on the generation of random paths between two points. The proposed work applies to many fields such as education, economics, computer science and AI, military, and other fields of applied sciences. Our work has spanned several phases, where in the first phase a novel computer algorithm to generate random paths between two points in space has been developed. The aim was to be able to generate paths between two points in real-time that cannot be predicted in advance. In the second phase, we have developed an ontology that describes the domain of discourse. The aim was two folds; firstly, to provide an optimized generation of best points that are closer to the target point. Secondly, to provide sharable, reusable ontological objects that can be deployed to other projects. We reinforced our solution by the initiation of several case studies that have been designed using and extending our work. One problem that we have faced in some cases is the existence of some obstacles between the starting and the ending point. For example, in our work towards the automation of a navigation system for drones, we faced some obstacles like trees, no flying zones, and buildings. This problem is also applicable to mobile robots and other unmanned vehicles, where fee-collision mobility is necessary. In this phase, we have reworked the algorithm to generate random paths between two points P0(x0, y0), Pn(xn, yn) with obstacles. Our generated random paths are placed within circles that are centered in Pn: c1, c2, …, cn-1, which passes thru the points P1, P2, …, Pn-1 respectively. Point Pi may approach Pn if it takes any position within circle c centered in Pn with radius PiPn and satisfies some constraints, discussed in detail in the paper, which insure that the selected paths do not fall within obstacles and reach the target point. we also classified the generated paths based on given properties such as the longest path, shortest path, and paths with some given costs. The resulted algorithms were very encouraging and leading to the applicability of real-life cases.

The aim of this paper is solving optimal control problems governed by non-local diffusion equations via a mesh-less method. The diffusion equation and in particular, the heat conduction equation is essential in sciences. This equation appears in many fields, such as engineering, electrostatic, and mathematics. For solving the mentioned optimal control problems, the method is established upon expanding of variables by the basis of Bezier functions. We apply, for the first time, the Bernstein approximation in solving an optimal control problem governed by the diffusion equation. A direct algorithm is given for solving this problem. Bernstein polynomials expand the trajectories and control functions with unknown control points. Then the optimal control problem is converted to a mathematical programming problem. By solving the mathematical programming problem, the approximated solution of trajectories and control are driven. The convergence of the method in approximating of the optimal control problem is proved. Some numerical examples for demonstrating the effectiveness of the method are included.