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optimization-methods

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Genetic algorithms, simulated annealing, particle swarm optimization, gradient-based methods, topology optimization, shape optimization, size optimization, and benchmark problems for AEC computational design

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# Optimization Methods for AEC Computational Design

## 1. Optimization in AEC Design

### The Role of Optimization

Optimization is the systematic process of finding the best solution from a set of feasible alternatives according to one or more criteria. In the Architecture, Engineering, and Construction (AEC) industry, optimization transforms design from an intuition-driven craft into a rigorous, evidence-based discipline that can explore thousands of alternatives in the time a human designer evaluates a handful.

Every AEC project embeds optimization problems whether practitioners recognize them or not. Selecting a column grid that minimizes steel tonnage, arranging rooms to maximize adjacency satisfaction, routing ductwork to minimize pressure loss, or shaping a facade to balance daylight and solar heat gain -- all are optimization problems with design variables, objectives, and constraints.

### Design Optimization vs. Mathematical Optimization

Mathematical optimization seeks a global or local extremum of a function subject to constraints, governed by theorems about convexity, differentiability, and feasibility. Design optimization in AEC adds layers of complexity:
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