OPTIMIZER
MICRO-GA · BLX-α
TOURNAMENT k = 3
80 GEN × POP 40

Stop tuning. Start solving.

Declare a coupling target. The optimiser evolves coil geometries — amax, Nr, Nt and turn spacing — toward it using a real-valued micro-GA. Multi-objective mode returns a Pareto front over k, η and power.

01RecipeFig. 01 — Optimisation recipe
01 · RECIPE

Declare the target.

Single-objective runs minimise |k_achieved − k_target|. Multi-objective runs maximise k while balancing efficiency and delivered power. Manufacturing rules from IPC-2221 (current density, trace spacing, wire diameter) are enforced as hard constraints.

OBJk → target · k+η+PCONSTRIPC-2221
OPTIMISER · RECIPEmaximise k(θ)subject to amax ∈ [5, 200] Nr ∈ [1, 30] Nt ∈ [1, 30] δ ∈ [0, 10] mm IPC-2221 wires
02ConvergenceFig. 02 — Best-fitness vs generation
02 · CONVERGENCE

Watch it converge.

A real-valued chromosome encodes geometry; BLX-α crossover blends parents inside an extended hyper-rectangle; Gaussian mutation perturbs offspring; tournament selection with k = 3 picks survivors. 80 generations of population 40 is the default budget.

CROSSOVERBLX-αSELECTtournament k = 3BUDGET80 × 40
k_target reachedBEST-FITNESS · |k − k_target|generation 0 → 80
03Search boundsFig. 03 — Sample iterations inside the feasible region
03 · SEARCH BOUNDS

Inside the feasible region.

Bounds: amax ∈ [5, 200] mm, Nr ∈ [1, 30], turn spacing δ ∈ [0, 10] mm, Nt ∈ [1, 30]. Multi-objective runs return an NSGA-II-style Pareto front of non-dominated geometries you can browse.

amax5 – 200 mmNr · Nt1 – 30δ0 – 10 mmPARETONSGA-II
FEASIBLE BOX · IPC-22215amax · mm200130Nramax ≤ 200 mm →↑ Nr ≤ 30
§

Let the solver find it.