| Many complex engineering problems and scientific research existing in the real world can be abstracted into optimization problems.Traditional optimization methods have serious limitations when applied to complex and difficult optimization problems.Over the years,with the development of a large number of algorithms based on artificial intelligence,biota sociality,or the laws of natural phenomena,optimization algorithms have become critical for many optimization applications.Among them,the dragonfly algorithm is a new swarm intelligence optimization algorithm based on the flight behavior of dragonflies,which has been favored by many researchers due to its simple parameters and easy implementation.However,the current related research is limited,and the optimization capability of the algorithm has yet to be improved.Therefore,this thesis focuses on how to improve the solution accuracy and convergence speed of the dragonfly algorithm,enhance the global search ability of the algorithm,and finally use it to achieve antenna instance optimization.The main research contents of this thesis are as follows:Firstly,this thesis addresses the problems of search space discretization,accuracy loss and the curse of dimensionality generated by the traditional binary algorithm using the transfer function,and proposes an improved angle modulated dragonfly algorithm(IAMDA)according to the characteristics of the dragonfly algorithm,which is used to improve the solution accuracy and speed up the convergence speed.In the proposed improved angle modulated dragonfly algorithm,an angle modulation mechanism is introduced to convert the complex binary optimization problem into a simpler continuous problem optimization.In order to further improve the performance of the algorithm,a new coefficient is introduced to control the degree of disturbance of the generating function in the angle modulation mechanism,which is beneficial to improve the global optimization ability and the stability of the algorithm.Then the performance is tested using the benchmark function and the 0-1 knapsack problems,and is validated with statistical tests,the numerical results show the superior performance of IAMDA.In addition,the topology optimization design of planar monopole antenna is carried out by IAMDA,in order to make the return loss value meets the design requirements in the specified frequency band,and the simulation example verifies the superiority of IAMDA in the antenna topology optimization problem.Secondly,this thesis proposes a multi-objective dragonfly algorithm based on competition mechanism(CMODA).By introducing an elite competition mechanism into the multi-objective dragonfly algorithm,so as to select potential dragonfly individuals in the dragonfly population,guide the population renewal,strengthen the advantages of elite individuals,and increase the diversity of the dragonfly population,which is beneficial to ensure a good distribution of solutions while improving the algorithm solution accuracy.CMODA is tested on the multi-objective test problems ZDT and DTLZ,and compared with three other multi-objective algorithms to verify the performance of the algorithm.The experimental results show the effectiveness of the proposed algorithm in solving multi-objective problems.In addition,CMODA is used for the multi-objective optimization design of a dual-band high-gain planar monopole antenna,the the obtained antenna structure not only satisfies the requirement of return loss value in the specified frequency bands,but also obtains high gain in these bands.It is also less computationally expensive,which verifies the effectiveness of CMODA in multi-objective antenna topology optimization. |
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