Abstract
A general Multi-objective evolutionary algorithm (MOEA) is presented to solve the antenna optimization problem with complex requirements. By analyzing the common requirements in the antenna design process, a strategy of calculating the difference between the indicator value and the threshold is applied to construct the objective units that can accurately evaluate the antenna performance and reconstruct the objective functions quickly. The fitness functions can solve the fuzzy requirements problem. An adaptive mechanism is designed for mutation and crossover operation in order to balance the convergence rate and the ability to avoid local extremes. In non-dominated sorting, the crowding calculation method is improved so that the individuals can be dispersed in the objective space. A demotion mechanism is designed for the selection operation to balance the importance of all objective functions and constraints. It can not only make the optimal solution closer to the actual requirement but also reduce the computation cost of the algorithm itself. Then this algorithm is applied to an ultra-wideband (UWB) dipole antenna with complex requirements as a case study. Analyzing the optimization results shows that all the indicators of the antenna optimized by the algorithm can well satisfy the actual requirements.
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