A highly competitive micro evolutionary algorithm to solve unconstrained optimization problems called μJADE (micro adaptive differential evolution), is adapted to deal with constrained search spaces. Two constraint-handling techniques (the feasibility rules and the ε-constrained method) are tested in μJADE and their performance is analyzed. The most competitive version is then compared against ...

-In this paper, a fast dynamical multi-objective evolutionary algorithm (DMOEA) based of the principle of the minimal free energy in thermodynamics, was developed to solve mechanical component design problems. Its performance was compared with the ε -constraint method and NSGA-II proposed by Deb et al. Simulation results show that the proposed evolutionary approach produces excellent solutions ...

The job shop scheduling problem (JSP) is one of the most difficult problems in traditional scheduling because any job consists of a set operations and also any operation processes by a machine. Whereas the operation is placed in the machine, it is essential to be considering setup times that the times strongly depend on the various sequencing of jobs on the machines. This research is developed ...

An optimal control problem is studied, in which the state is required to remain in a compact set S. A control feedback law is constructed which, for given ε > 0, produces ε-optimal trajectories that satisfy the state constraint universally with respect to all initial conditions in S. The construction relies upon a constraint removal technique which utilizes geometric properties of inner approxi...

We consider the problem of packing squares with profits into a bounded square region so as to maximize their total profit. More specifically, given a set L of n squares with positive profits, it is required to pack a subset of them into a unit size square region [0;1℄ [0;1℄ so that the total profit of the squares packed is maximized. For any given positive accuracy ε > 0, we present an algorith...

We present an approximation algorithm for the shortest descending path problem. Given a source s and a destination t on a terrain, a shortest descending path from s to t is a path of minimum Euclidean length on the terrain subject to the constraint that the height decreases monotonically as we traverse that path from s to t. Given any ε ∈ (0, 1), our algorithm returns in O(n log(n/ε)) time a de...

BACKGROUND Left ventricular (LV) function is dependent on load, intrinsic contractility and relaxation with a variable impact on specific mechanics. Strain (ε) imaging allows the assessment of cardiac function; however, the direct relationship between volume and strain is currently unknown. The aim of this study was to establish the impact of preload reduction through head-up tilt (HUT) testing...

Many previous Sum-of-Squares (SOS) lower bounds for CSPs had two deficiencies related to global constraints. First, they were not able to support a “cardinality constraint”, as in, say, the Min-Bisection problem. Second, while the pseudoexpectation of the objective function was shown to have some value β, it did not necessarily actually “satisfy” the constraint “objective = β”. In this paper we...