evomo.algorithms.lsmof#

Attributes#

algo

Classes#

LSMOF

Initialize two-phase weight-space optimization and NSGA-II refinement.

Module Contents#

class evomo.algorithms.lsmof.LSMOF(pop_size: int, n_objs: int, lb: torch.Tensor, ub: torch.Tensor, **kwargs)#

Bases: evox.core.Algorithm

Initialize two-phase weight-space optimization and NSGA-II refinement.

Parameters:

  • pop_size (int) – Required. Requested number of candidate solutions. Use a positive integer; algorithm-specific minimums and reference-vector sampling are described below.

  • n_objs (int) – Required. Number of objectives, matching the second dimension of the problem’s objective tensor. Objectives are minimized; this library targets two or more objectives.

  • lb (torch.Tensor) – Required. Lower decision bounds of shape (D,), where D is the number of decision variables. Use floating-point bounds with the same shape, dtype and device as ub, and lb <= ub elementwise.

  • ub (torch.Tensor) – Required. Upper decision bounds of shape (D,). The decision dimension is inferred from the bounds, rather than passed separately. Match lb in shape, dtype and device.

  • kwargs (dict) – Default: {}. Optional settings: wD=5 (reference solutions), SubN=20 (weight population size), wmax=0.1 (decision reconstruction scale), and max_fe=10000 (evaluation horizon). Other keywords are ignored. The full meanings and limits are listed below.

Additional keyword settings

wD (int, default 5)

Reference solution count for building directions from both bounds. Use a positive value no larger than the available distinct population. The reconstruction assumes that exactly this many reference solutions are selected.

SubN (int, default 20)

Positive weight-population size. The first phase evaluates 2 * wD * SubN reconstructed decision vectors per step.

wmax (float, default 0.1)

Positive scale multiplying weighted unit-direction offsets in decision reconstruction. This is a decision- space step scale, not a fraction of the evaluation budget.

max_fe (int, default 10000)

Positive evaluation horizon. The second phase begins when the evaluation counter reaches max_fe // 2; the initial evaluations are included. Termination remains controlled by the caller.

Note

Use this algorithm through a workflow that connects the problem’s evaluation method. Call workflow.init_step() before workflow.step() or compiling the step. The current evaluation path expects an objective tensor of shape (B, n_objs). It does not consume a (fitness, constraint_violation) tuple.

Tensor allocation uses lb.device. There is no explicit device parameter; prepare both bounds on the intended device.

init_step() → None#

Evaluate the initial population and initialize algorithm state.

Invoke through workflow.init_step() before the first optimization step.

Returns:

None; results are stored in algorithm state.

step() → None#

Advance optimization and update population and fitness state in place.

Invoke through workflow.step() after initialization. The caller controls termination.

Returns:

None; results are stored in algorithm state.

evomo.algorithms.lsmof.algo#