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Non-orthogonal multiple access (NOMA) and coordinated multi-point (CoMP) are two fundamental techniques considered for the fifth generation (5G) of wireless communications. In this paper, a hybrid satellite-unmanned aerial vehicle (UAV) relay network (HSURN) is proposed where the UAV relays (URs) employ CoMP transmission to serve the terrestrial users (UEs). Furthermore, all UEs associated with the CoMP-URs form a single NOMA cluster. For this model, an optimization problem is formulated subject to the minimum quality of services (QoSs) requirements of the UEs, transmission power budgets and, successive interference cancellation (SIC), to select URs and allocate their transmission powers for the energy efficiency (EE) maximization. With this insight, first, a computationally efficient sub-optimal UR selection scheme is proposed. Then, the powers are allocated to the selected URs via the Lagrange multipliers optimization (LMO) method. Due to the non-convex nature of the considered problem, it is relatively difficult to be solved. Hence, a metaheuristic teaching-learning-based optimization (TLBO) algorithm is employed to achieve an efficient solution. Simulation results are provided to verify the effectiveness of the proposed sub-optimal relay selection scheme and the TLBO-based power allocation method compared to the LMO conventional method. Besides, the obtained results also reveal that the CoMP-NOMA transmission in the proposed scenario significantly improves the spectral efficiency (SE) and outage probability (OP) of the system compared to non-comp NOMA transmission case.Index Terms-Non-orthogonal multiple access (NOMA), Coordinated multi-point (CoMP) transmission, energy efficiency (EE), UAV relay selection, satellite terrestrial network, outage probability (OP). I. INTRODUCTIONThe 5G-satellite networks in the integrated architecture have emerged as a valuable infrastructure to meet the future radio access of smart devices. Combining satellite components into wireless systems is not only an indispensable way to provide seamless coverage and large capacity for users all over the world but also to ensure high QoS expectations [1]. Mobile satellite networks have been viewed as a promising technique for the smart grid, internet-of-thing (IoT), wireless sensor

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This paper introduces a new hybrid hill-climbing algorithm (HHC) for solving the Economic Dispatch (ED) problem. This algorithm solves the ED problems with a systematic search structure with a global search. It improves the results obtained from an evolutionary algorithm with local search and converges to the best possible solution that grabs the accuracy of the problem. The most important goal of economic load dispatch is the optimal allocation of each generator's contribution to provide the load and reduce the costs of active units in the power system. This is generally due to presence of the nonlinear factors and limitations, such as the effect of the steam inlet valve (valve point effect (VPE)), the balance between the power generation and power demand of the system, the prohibited operating zones (POZS), power generation limits, ramp rate limits, and transmission losses. This algorithm is implemented on three 13-unit, 15-unit and 40-unit test systems with different operating conditions, and also for the same three test systems in combination with the evolutionary PSO algorithm. The simulation results show the efficiency of the proposed algorithm in solving ED problems.

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