A Study On Intelligence Optimization Algorithms And Its Applications Inantenna Designs

Intelligence optimization algorithms, which have simple principle, strong robustness and highsuccess rate of global optimization, are considered as a kind of efficient optimization algorithms forsolving optimization problems. And they are widely used in a variety of complex optimizationproblems such as the field of antenna optimization. With the aid of intelligence optimizationalgorithms for antenna optimization to make the antenna performance parameters meet therequirements of specific technical indicators, the efficiency of antenna design has been greatlyimproved.The arithmetic learning epitome of original Team Progress Algorithm(TPA) is replaced bygeometry learning epitome and dynamic weight learning epitome respectively in this thesis. With20benchmark test functions to the new modified algorithms, the optimization results show that theperformance of the modifications has been improved. Then Particle Swarm Optimization(PSO) isalso tested as a comparison algorithm. Numerical results verify the high efficiency of labor-divisioncooperation of TPA.Additionally, a plug-in optimizer based on intelligence optimization algorithm combined withsimulation software is introduced in this thesis. And the modified TPA of geometry learningepitome is used to increase the impedance bandwidth both in low and high frequency of a planarprinted monopole antenna successfully. At last, PSO is also used to optimize the same antenna tocompare with the former result under the condition of the same objective function and the sameamount of computation. It is fully proved the feasibility of plug-in optimizer as well as thesuperiority and efficiency of labor-division cooperation of TPA.