The synthesis of a simple electronically reconfigurable annular ring monopole antenna using a designing optimisation process based on particle swarm optimisation (PSO) and artificial bee colony (abc) algorithms is pro...
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The synthesis of a simple electronically reconfigurable annular ring monopole antenna using a designing optimisation process based on particle swarm optimisation (PSO) and artificial bee colony (abc) algorithms is proposed. Several antenna dimensions are selected as objective functions of PSO and abc and their best solutions are considered as the optimised dimensions of the antenna geometry. Two radio-frequency p-i-n diodes, connecting the antenna feeding line to two microstrip stubs are used to change the antenna frequency response from ultra-wideband (UWB), from 3.1 to 10.6 GHz, to narrowband (NB) operation about 5.8 GHz. The antenna design and simulation are performed using Ansoft high-frequency structure simulator software. PSO and abc algorithms are written in Java language. Thereafter, antenna prototypes are fabricated and measured for validation purposes. Simulation and measurement results are obtained showing good agreement. The measured optimised impedance bandwidths of the UWB and NB bands are up to 128 and 23%, respectively. Additionally, simulated and measured radiation patterns are very similar when the reconfigurable antenna is operating in OFF-state (UWB sensing antenna) and in ON-state (NB transmitting antenna) modes, indicating the proposed antenna geometry as a promising candidate for applications such as UWB and cognitive radio.
Coloured travelling salesman problem (CTSP) is an extended model of multiple travelling salesman problems (MTSPs), as one kind of problem in combination optimisation problems, which has been applied to many real-world...
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Coloured travelling salesman problem (CTSP) is an extended model of multiple travelling salesman problems (MTSPs), as one kind of problem in combination optimisation problems, which has been applied to many real-world planning problems such as multi-machine engineering system (MES). Population-based algorithms such as genetic algorithm (GA) and simulated annealing (SA) algorithm are only used to solve small- or medium-scale cases in which the city number is <2000. Furthermore, in term of the convergence speed and solution quality, their performances have further improvement space. Since many real-world problems can be modelled by large-scale CTSP, it is necessary to study better algorithms to solve large-scale CTSP. Since artificial bee colony algorithm (abc) can show good performance in solving combination optimisation problems, and therefore this study applied improved abc algorithms to solve large-scale CTSP. The modified abc algorithms use generating neighbourhood solution (GNS) to improve abc for this problem, two limitation and optimisation conditions are applied into GNS during the process of reinserting cities. The extensive experiments verify that the improved algorithms can demonstrate better solution quality than the compared algorithms for large-scale CTSP.
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