We present a new series of distributed constraint satisfaction algorithms, the distributed breakout algorithms, which is inspired by local search algorithms for solving the constraint satisfaction problem (CSP).
For solving the (constraint) minimisation problem, we use the R package nloptr [ 21].
An algorithm for solving the proposed design constraints by combining a nested bilinear-terms-solver approach and a nonlinear-optimization-based cone complementary linearization method is provided.
We model the day-ahead scheduling of electric power systems as a mixed-integer linear programing (MILP) problem for solving the hourly security-constraint unit commitment (SCUC).
However, it is only suitable for solving the driving forces/torques and constraint forces/moments of passive overconstrained PMs with collinear constraint forces or coaxial constraint moments.
The aim of this paper is to propose an exact approach based on a Branch-and-Cut algorithm for solving the LRP with capacity constraints on depots and vehicles.
To model the explored optimal doping profile into a GP problem, the subthreshold swing is formulated as an objective function and the on- and off-state currents are considered as constraints for solving the corresponding optimal doping profile.
The determined metabolic flux ratios were used as additional constraints for solving the metabolic network following a C constrained flux balancing approach similarly to a previous approach [ 88].
Experimental results show that the proposed algorithm is an effective method for solving the ORPP with mass balance constraint.
The computational results show that the proposed algorithm is an effective method for solving the circular packing problem with performance constraints of equilibrium.
