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Go to Editorial ManagerIn this paper, a compact ultra-wide band (UWB) printed patch antenna is designed and optimized using four biologically and plant inspired optimization algorithms. These algorithms are the newly adopted Moss Rose Optimization Algorithm (MROA), Runner Root Algorithm (RRA), Sunflower Optimization Algorithm (SFOA) and Particle Swarm Optimization (PSO). These algorithms are modified in an optimizer software, which merges the attributes of the design of electromagnetic environment of CST Microwave Studio with those of the technical programming environment of MATLAB. A compact (12 × 21.5) mm 2 printed patch antenna has been proposed and simulated over the whole UWB frequency range using these four optimization algorithms. The simulation results show the superiority of the antenna design using MROA, which has the widest covered frequency range, the lowest reflection coefficient and the lowest standing wave ratio.
Nowadays, it is crucial to assess power system contingencies resulting from line outages or generator failures, as they might cause breaches of system constraints. This is a vital part of ensuring the security of modern power supplies. Another hindrance to providing electricity to consumers is the increased system losses and voltage fluctuations resulting from increased demand and diminished power generation capacity. The DG connection is a crucial subject regarding these harmful consequences. This study is focused on clarifying the effect of distribution generators (DG) on mitigating congestion in electrical power transmission lines, minimizing power losses, and enhancing the voltage profile of the Iraqi national grid system. An optimization method is used to identify the optimal size and position based on fitness indicators such as voltage, power losses, and line congestion. The PSO algorithm is executed as proposed. The outcomes illustrate the effectiveness of the proposed technique for estimating the optimal size and placement of distributed generators (DG). At the same time, it reduces congestion and improves the voltage level of the bus. The proposed technique was implemented using the MATLAB/R2018a programming language.
Crow Search Algorithm is an innovative meta- heuristic optimization algorithm. In this paper, chaotic maps are combined into Crow Search Algorithm to increase its global optimization. Ten variant chaotic maps are used and the Tent map is found as the best choices for high dimensional problems. The novel Chaotic Crow Search Algorithm is relied on the substitution of a random location of search space and the awareness parameter of crow with chaotic sequences. The results show that the chaotic maps are able to enhance the performance of the Crow Search Algorithm. Also the novel Chaotic Crow Search Algorithm outperforms the conventional Crow Search Algorithm, first version of Chaotic Crow Search Algorithm, Genetic Algorithm, and Particle Swarm Optimization Algorithm from the point view of speed convergence and the function dimensions.