Adaptive penalty function methods (APFMs) are promising constraints handling techniques. In an APFM, a parameter which balances constrains’ violations and objective values is adaptively adjusted. This work modifies APFM that uses near feasibility threshold (NFT), portion around the feasible region where infeasible solutions considered as good ones, for constrained multiobjective optimization. T...

This paper addresses the Tardy/Lost penalty minimization on a single machine. According to this penalty criterion, if the tardiness of a job exceeds a predefined value, the job will be lost and penalized by a fixed value. Besides its application in real world problems, Tardy/Lost measure is a general form for popular objective functions like weighted tardiness, late work and tardiness with reje...

where f : Rn → R and g : Rn → Rp are nonlinear continuous functions and Ω = {x ∈ Rn : −∞ < l ≤ x ≤ u < ∞}. Problems with equality constraints, h(x) = 0, can be reformulated into the above form by converting into a couple of inequality constraints h(x)− β ≤ 0 and −h(x)− β ≤ 0, where β is a small positive relaxation parameter. Since we do not assume that the objective function f is convex, the pr...

Considering a reservoir with periodic states and different cost functions with penalty, its release rules can be modeled as a periodic Markov decision process (PMDP). First, we prove that policyiteration algorithm also works for the PMDP. Then, with policyiteration algorithm, we obtain the optimal policies for a special aperiodic reservoir model with two cost functions under large penalty and g...

For constrained nonsmooth optimization problems, continuously diierentiable penalty functions and barrier functions are given. They are proved exact in the sense that under some nondegeneracy assumption, local optimizers of a nonlinear program are also optimizers of the associated penalty or barrier function. This is achieved by augmenting the dimension of the program by a variable that control...

In many evolutionary algorithms, as fitness functions, penalty functions play an important role. In order to solve zero-one nonlinear optimization problems, a new objective penalty function is defined in this paper and some of its properties for solving integer nonlinear optimization problems are given. Based on the objective penalty function, an algorithm with global convergence for integer no...

This paper discusses a new roughness penalty for use in estimation problems including image estimation problems. It is one of a new class of penalty functions for use in estimation and image regularization that has recently been proposed. These functions penalize the discrepancy between an image and a shifted version of itself; here the discrepancy measure is the I-divergence. This penalty is c...

In the Multi Input Multi Output (MIMO) antenna system, it is known that the Linear Minimum Mean Squared Error (MMSE) receiver is equivalent to Tikhonov regularization. Given that, we develop a family of generalized receivers based on regularization with different penalty functions that penalize the received symbols outside the convex hull of the modulating constellation. For illustration purpos...