evomo.algorithms.moead_du#

Attributes#

algo

Classes#

MOEADDU

Initialize the MOEADDU population and optimization state.

Module Contents#

class evomo.algorithms.moead_du.MOEADDU(pop_size: int, n_objs: int, lb: torch.Tensor, ub: torch.Tensor, T: int = 20, delta: float = 0.9, K: int = 5, **kwargs)#

Bases: evox.core.Algorithm

Initialize the MOEADDU population and optimization state.

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.

  • T (int) – Default: 20. Number of nearest weight vectors in each mating neighborhood, including the subproblem itself. Use a positive integer. Values above the sampled population size are capped to that size.

  • delta (float) – Default: 0.9. Probability in [0, 1] of drawing mating parents from the current subproblem’s neighborhood. The remaining draws use the full population. This controls mating, not the replacement limit.

  • K (int) – Default: 5. Number of subproblems considered for distance-based updating per offspring, selected by cosine similarity to weight vectors. Use 1 <= K <= N for sampled population size N. This is separate from mating neighborhood size T.

  • kwargs (dict) – Default: {}. Extra keyword arguments are accepted for constructor compatibility but are not read by this implementation. In particular, passing device=... here does not move tensors; place both bounds on the intended device before construction.

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.

Reference-vector sampling can change the requested population size. Read self.pop.shape[0] for the actual size.

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.moead_du.algo#