Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

As one of the key technologies in the fifth generation of mobile communications, massive multi-input multi-output (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radio-frequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users, and analyze their impact on EE in the uplink and downlink of a single-cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero-forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single-cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single-cell perfect CSI scenario. In the single-cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm

Application of optimization techniques to solve overcurrent relay coordination.

Masters Degree. University of KwaZulu- Natal, Durban.Distribution systems continues to grow and becoming more complex with increasing operational challenges such as protection miscoordination. Initially, conventional methods were favoured to optimize protection coordination; however, the implementation process is laborious and time-consuming. “Therefore, recent studies have adopted the utilisation of particle swarm optimization and genetic algorithms to solve overcurrent relay coordination problems and maximise system selectivity and operational speed. Particle swarm optimization and genetic algorithms are evolutionary algorithms that at times suffer from premature convergence due to poor selection of control parameters. Consequently, this thesis aims to present a comprehensive sensitivity analysis to evaluate the effect of the discrete control parameters on the performance of particle swarm optimizer and genetic algorithms, alternatively on the behaviour of overcurrent relays. The main objectives of this research work also include modelling and simulation of distribution system protection scheme, employment of evolutionary algorithms with control parameters that perform efficiently and effectively to maximise protection coordination between relays, optimize relay operating time and maintain the stipulate coordination time interval, and lastly, to outline future recommendations. The distribution network understudy was modelled and simulated on a real-time digital simulator to validate protection settings, and the verification of evolutionary algorithms performance was displayed on Matlab/Simulink. An extensive parametric sensitivity analysis was conducted to understand the impact of the individual control parameters and their respective influence on the performance of evolutionary algorithms. The findings indicate that particle swarm optimization is more sensitive to inertia weight and swarm size while the number of iterations has minimal effect. The results also depict that genetic algorithms’ performance is mostly influenced by crossover probability, mutation probability, and population size. Sensitivity analysis results were verified by comparing the performance of particle swarm optimizer with genetic algorithms, which demonstrated that particle swarm optimization performs efficiently and robustly in solving the considered problem, especially in terms of convergence speed. Furthermore, overcurrent relays were more sensitive, selective, and the operational speed was reduced for particle swarm optimizer compared to other algorithms. The optimal protection coordination achieved using particle swarm optimization showed superiority of the algorithm, its ability to circumvent premature convergence, consistency, and” efficiency

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Development and application of an optimisation architecture with adaptive swarm algorithm for airfoil aerodynamic design

The research focuses on the aerodynamic design of airfoils for a Multi-Mission Unmanned Aerial Vehicle (MM-UAV). Novel shape design processes using evolutionary algorithms (EA) and a surrogate-based management system are developed to address the identified issues and challenges of solution feasibility and computational efficiency associated with present methods. Feasibility refers to the optimality of the converged solution as a function of the defined objectives and constraints. Computational efficiency is a measure of the number of design iterations needed to achieve convergence to the theoretical optimum. Airfoil design problems are characterised by a multi-modal solution topology. Present gradient-based optimisation methods do not converge to an optimal profile, hence solution feasibility is compromised. Population-based optimisation methods including the Genetic Algorithm (GA) have been used in the literature to address this issue. The GA can achieve solution feasibility, yet it is computationally time-intensive, hence efficiency is compromised. Novel EAs are developed to address the identified shortcomings of present methods. A variant to the original Particle Swarm Optimisation algorithm (PSO) is presented. Novel mutation operators are implemented which facilitate the transition of the search particles toward a global solution. The methodology addresses the limited search performance of the original PSO algorithm for multi-modal problems, while maintaining acceptable computational efficiency for aerodynamic design applications. Demonstration of the developed principles confirmed the merits of the proposed design approach. Airfoil optimisation for a low-speed flight profile achieved drag performance improvement that is lower than a off-the-shelf shape designed for the intent role. Acceptable computational efficiency is achieved by restricting the optimisation phase to promising solution regions through the development of a novel, design variable search space mapping structure. The merit of the optimisation framework is further confirmed by transonic airfoil design for high-speed missions. The wave drag of the established optima is lower than the identified, off-the-shelf benchmark. Concurrently significant computational time-savings are achieved relative to the design methodologies present in the literature. A novel surrogate-assisted optimisation framework by the definition of an Artificial Neural Network with a pattern recognition model is developed to further improve the computational efficiency. This has the potential of enhancing the aerodynamic shape design process. The measure of computational efficiency is critical in the development of an optimisation algorithm. Airfoil design simulations presented required 80\% fewer design iterations to achieve convergence than the GA. Computational time-savings spanning days was achieved by the innovative algorithms developed relative to the GA. Hence, computational efficiency of the developed processes is confirmed. Aircraft shape design simulations involve three-dimensional configurations which require excessive computational effort due to the use of high-fidelity solvers for flow analysis in the optimisation process. It is anticipated that the confirmed computational efficiency performance of the design structure presented on two-dimensional cases will be transferable to three-dimensional shape design problems. It is further expected that the novel principles will be applicable for analysis within a multidisciplinary design structure for the development of a MM-UAV