The problem of clustering a set of data is a textbook machine learning problem, but at the same time, at heart, a typical optimization problem. Given an objective function, such as minimizing the intra-cluster distances or maximizing the inter-cluster distances, the task is to find an assignment of data points to clusters that achieves this objective. In this paper, we present a constraint prog...

Dynamic distributed constraint optimization problems (Dynamic DCOPs) are useful in modeling various distributed combinatorial optimization problems that are dynamically changing over time. Previous attempts assume that it is possible for agents to take on a new solution each time the problem changes. However, in some applications, it is necessary to commit to a single solution at the start of t...

Many combinatorial (optimisation) problems have natural models based on, or including, set variables and set constraints. This modelling device has been around for quite some time in the constraint programming area, and proved its usefulness in many applications. This paper introduces set variables and set constraints also in the local search area. It presents a way of representing set variable...

Dynamic, partial centralization has received a considerable amount of attention in the distributed problem solving community. As the name implies, this technique works by dynamically identifying portions of a shared problem to centralize in order to speed the problem solving process. Currently, a number of algorithms have been created which employ this simple, yet powerful technique to solve pr...

In this paper, a displacement-constrained volume-minimizing topology optimization model is present for two-dimensional continuum problems. The new model is a generalization of the displacement-constrained volume-minimizing model developed by Yi and Sui [1] in which the displacement is constrained in the loading point. In the original model the displacement constraint was formulated as an equali...

Article history: Received 6 November 2011 Received in revised form 28 August 2012 Accepted 4 September 2012 Available online 7 September 2012

Constraints pervade our everyday lives and are usually perceived as elements that limit solutions to the problems that we face (e.g., the choices we make everyday are typically constrained by limited money or time). However, from a computational point of view, constraints are key components for efficiently solving hard problems. In fact, constraints encode knowledge about the problem at hand, a...

Efficient and intuitive characterization of biological big data is becoming a major challenge for modern bio-OMIC based scientists. Interactive visualization and exploration of big data is proven to be one of the successful solutions. Most of the existing feature selection algorithms do not allow the interactive inputs from users in the optimizing process of feature selection. This study invest...

We present a optimization formulation for discrete binary CSP, based on the construction of a continuous function A(P) whose global maximum represents the best possible solution for that problem. By the best possible solution we mean either (i) a solution of the problem, if it is solvable, or (ii) a partial solution violating a minimal number of constraints, if the problem is unsolvable. This a...