The utility of numerical codes is greatly enhanced if they can be used in design, a situation which typically involves iterative optimization algorithms. An attractive way is to use gradient-based algorithms developed for solving nonlinear programming (NLP) problems. In this paper, we examine the performance of a general sequential quadratic programming (SQP) optimization algorithm for designing patch antennas in conjunction with a finite element-boundary integral code. Recently, genetic

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... hms (GAs) have been examined for array design and absorber optimization [5] [6] [7] . However, GAs, although robust, require large number of function evaluations to complete the optimization study. Also GAs are more suitable for discrete variable problems. In contrast, antenna simulations rely on complex, computationally intensive codes, which generate continuous functions. It may therefore be impractical to generate a sufficiently large sample space for carrying out an optimization study using GAs. An alternative optimization algorithm is the Sequential Quadratic Programming (SQP) methods, suitable for continuous nonlinear objective functions such as the input impedance, gain, pattern shape and so on, with both equality and inequality constraints. Convergence is typically achieved in a few iterations, and therefore their interface with rigorous (but expensive) numerical antenna analysis codes is much more practical. SQP and other similar algorithms are routinely used for large structural design problems involving finite element analysis [8], and thus we can benefit from the extensive experience available in other disciplines. In this paper, we examine the performance of a general SQP code [9] for designing patch antennas in conjunction with a finite element-boundary integral code [10] . Both are rigorous general purpose codes. The main point of the paper is to examine the suitability of SQP for antenna parameter optimization to achieve the design objectives subject to constraints. We will illustrate the performance of the optimizer using a few illustrative examples from simple to more complex.