Leveled-commitment contracting is a negotiation protocol in which an agent is allowed to be freed from a contract at the cost of paying a penalty to the other contract party. While there has been some related work on analyzing agents’ strategic behavior in leveledcommitment contracting and applying decommitment contracting in practical resource allocation problems, decommitment penalties are ex...

Penalty (mean-drop) analysis is used by market researchers and product developers to gain an understanding of the product attributes that most affect liking, purchase interest or any other product-related measure. Product attributes used in penalty analysis are measured with “Just -about-right” (JAR) scales. These are categorical scales in which some points represent “too little” of a particula...

In this paper we discuss the use of non-stationary penalty functions to solve general nonlinear programming problems (NP) using real-valued GAs. The non-stationary penalty is a function of the generation number; as the number of generations increases so does the penalty. Therefore, as the penalty increases it puts more and more selective pressure on the GA to nd a feasible solution. The ideas p...

In this paper, we considered the role of penalty when the higher-order conjugate gradient method (HCGM) was used as a computational scheme for the minimization of penalised cost functions for optimal control problems described by linear systems and integral quadratic costs. For this family of commonly encountered problems, we find out that the conventional penalty methods require very large pen...

This paper describes a variant of simulated annealing incorporating a variable penalty method to solve the traveling salesman problem with time windows (TSPTW). Augmenting temperature from traditional simulated annealing with the concept of pressure (analogous to the value of the penalty multiplier), compressed annealing relaxes the time window constraints by integrating a penalty method within...

Many image denoising methods can be characterized as minimizing “loss + penalty,” where the “loss” measures the fidelity of the denoised image to the data, and the “penalty” measures the smoothness of the denoising function. In this paper, we propose two models that use the L1-norm of the pixel updates as the penalty. The L1-norm penalty has the advantage of changing only the noisy pixels, whil...

Evolutionary algorithms are becoming increasingly valuable in solving large-scale, realistic engineering multiobjective optimization problems, which typically require consideration of conflicting and competing design issues. A criticism of Evolutionary Algorithms might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search ...

In this paper we discuss the use of non-stationary penalty functions to solve general nonlinear programming problems (NP) using real-valued GAS. The non-stationary penalty is a function of the generation number; as the number of generations increases so does the penalty. Therefore, as the penalty increases it puts more and more selective pressure on the GA to find a feasible solution. The ideas...

This paper describes a variant of simulated annealing incorporating a variable penalty method to solve the traveling salesman problem with time windows (TSPTW). Augmenting temperature from traditional simulated annealing with the concept of pressure (analogous to the value of the penalty multiplier), compressed annealing integrates a penalty method with heuristic search to address the TSPTW. Co...

In this paper we propose a power penalty approach to linear complementarity problems (LCP) in a finite dimensional space. This approach is based on approximating the LCP by a nonlinear equation with a power penalty term. We prove that the solution to this equation converges to that of the LCP at a rate equivalent to the reciprocal of the power used in the penalty term when the penalty parameter...