RF designers rely on HFSS to design, optimize, and debug their projects. Optimization is essential but often computationally costly. Choosing the right optimizer is critical—especially when the model has many variables, wide ranges, and aggressive targets. Before launching a full optimization, engineers may explore the design space—for example, enabling HFSS’s derivative post-processing to narrow down the parameters that matter most.
Below we summarize the optimizers available in HFSS. Some are gradient-based; others use stochastic/heuristic search. The first table identifies each optimizer’s search method (gradient vs. non-gradient) and gives a relative speed indication.
| Search Type | Gradient-Based? | Continuous / Discrete Support | Speed | |
| Screening (Search-based) | Direct Sampling | No | Continuous & Discrete | Very Fast |
| Multi-Objective Genetic Algorithm (Random-search) | Evolutionary | No | Continuous & Discrete | Slow |
| Nonlinear Programming by Quadratic Lagrangian (Gradient) | Gradient-based | Yes | Continuous | Fast |
| Mixed-Integer Sequential Quadratic Programming (Gradient & Discrete) | Hybrid | Yes | Continuous + Discrete | Moderate |
| Adaptive Multiple-Objective (Random Search) | Evolutionary | No | Continuous & Discrete | Slow |
| Adaptive Single-Objective (Gradient) | Adaptive Gradient | Yes | Continuous | Moderate |
| MATLAB | External | Depends | Depends | Depends |
| Sequential Nonlinear Programming (Gradient) | Gradient-based | Yes | Continuous | Fast |
| Sequential Mixed Integer Nonlinear Programming (Gradient & Discrete) | Hybrid | Yes | Continuous + Discrete | Moderate |
| Quasi-Newton (Gradient) | Gradient-based | Yes | Continuous | Fast |
| Pattern Search (Search-based) | Derivative-free | No | Continuous + Discrete | Moderate |
| Genetic Algorithm (Random Search) | Evolutionary | No | Continuous & Discrete | Slow |
The next table details each technique and its ideal use cases. Some excel with a single objective or a small set of variables, while others are better suited to complex or multi-constraint goals.
| Best For | Notes / Characteristics | |
| Screening (Search-based) | Finding rough parameter trends before real optimization | Uses uniform sampling; no refinement; helps identify sensitive parameters |
| Multi-Objective Genetic Algorithm (Random-search) | Complex, non-convex, multi-objective designs | Simulates natural evolution (selection, crossover, mutation); robust to local minima |
| Nonlinear Programming by Quadratic Lagrangian (Gradient) | Smooth, differentiable problems | Uses Lagrange multipliers and quadratic subproblems; may fail with discontinuities |
| Mixed-Integer Sequential Quadratic Programming (Gradient & Discrete) | Mixed-variable designs (geometry + on/off options) | Combines gradient methods with integer variable handling |
| Adaptive Multiple-Objective (Random Search) | Multi-objective trade-offs (e.g., gain vs bandwidth) | Builds Pareto fronts; balances exploration and exploitation |
| Adaptive Single-Objective (Gradient) | Single-goal, smooth problems | Adjusts step size adaptively; converges faster than basic gradient |
| MATLAB | Custom algorithms | Allows you to call MATLAB optimization functions if installed |
| Sequential Nonlinear Programming (Gradient) | Smooth, single-objective problems | Classic constrained optimization; sensitive to initial guess |
| Sequential Mixed Integer Nonlinear Programming (Gradient & Discrete) | Mixed topologies | Allows some discrete (integer) parameter handling |
| Quasi-Newton (Gradient) | Convex, differentiable problems | Approximates Hessian matrix; faster convergence than basic gradient |
| Pattern Search (Search-based) | Non-smooth problems, few variables | Explores parameter space by pattern expansion/contraction |
| Genetic Algorithm (Random Search) | Nonlinear, discontinuous objectives | Robust to local minima, |
The final table guides RF designers in choosing an optimizer based on the objective. For PCB and many antenna problems, gradient-based methods are usually preferred; for filter design, quasi-random search tends to work best.
| Design Type | Recommended Optimizer |
| Smooth & continuous | Quasi-Newton or Sequential Nonlinear Programming |
| Contains discrete variables | Mixed-Integer SQP or Pattern Search |
| Multiple conflicting goals (e.g., Gain vs Bandwidth) | Multi-Objective Genetic Algorithm or Adaptive Multiple-Objective |
| Discontinuous response (HFSS field jumps, mesh changes) | Genetic Algorithm or Pattern Search |
| Quick exploration / initial screening | Screening |
| Custom / advanced control | MATLAB interface |
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