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Global Constraints in Distributed Constraint Satisfaction and Optimization

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The Computer Journal
Year: 2014 | Volume: 57, Issue: 6 | Journal Article |
Global constraints are an essential component in the efficiency of centralized constraint programming. We propose to include global constraints in distributed constraint satisfaction problem (DisCSP) and distributed constraint optimization problem (DCOP). We detail how this inclusion can be done, considering different representations for global constraints (direct, nested, binary). We explore the ...Show More

Global Constraints in Distributed Constraint Satisfaction and Optimization

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Year: 2014 | Volume: 57, Issue: 6 | Journal Article |
Constraint programming is a powerful tool for modeling and solving various problems. Especially, soft constraints are useful since they enable the treatment of over-and under-constrained real-world problems by relaxing conflicting constraints and introducing default constraints. Constraint hierarchies provide a soft constraint framework that introduces hierarchical preferences called strengths. In...Show More
This paper proposes a constraint handling technique for multiobjective evolutionary algorithms based on an adaptive penalty function and a distance measure. These two functions vary dependent upon the objective function value and the sum of constraint violations of an individual. Through this design, the objective space is modified to account for the performance and constraint violation of each in...Show More
Multidisciplinary collaborative decision of product design is modeled as a constraint satisfaction problem and a new hybrid algorithm which combined chaotic search with immune algorithm is developed to solve the model. The meticulous searching capability of chaotic algorithm can keep immune algorithm from stunning into local optimum and find global optimal design solution. For the fitness evaluati...Show More
The work deals with solving one of the important problems of Scheduling Theory – the Open Pit Mine Production Scheduling Problem. To solve this problem, methods of Mixed Integer Linear Programming are widely used, the fundamental disadvantage of which is that they impose increased requirements on the amount of RAM required to store the model and implement calculations. This article presents the re...Show More
Collaborative design can be characterized by different level constraints that describe basic elements of design process such as human, information and resource. In design, satisfied solutions to constraints must occur to arrive at a final design result. Few collaborative design systems exist for adequately representing constraints, and formally combining them from different disciplines. The main c...Show More
Constraints provide an effective means for the high-level modeling and reasoning of various problems. In particular, soft constraints are useful since they treat over-constrained problems that naturally arise in real-life applications. Therefore, researchers have been exploring frameworks for soft constraints. The framework of semiring-based constraint satisfaction problems (CSPs) can express many...Show More
For the quadratic programming problems with both equality and inequality constraints, an improved neural network is proposed based on the Lagrange function reconstructed based on the saddle point theorem of optimization theory. The proposed neural network has less neuron quantity than the traditional method with slack variables does. The stability and convergency of the proposed neural network is ...Show More
A new method is proposed to deal with constrained optimization problem to overcome certain disadvantages of the current methods. The proposed algorithm adopts a new constraint-handling method to deal with constraints and does not introduce penalty parameters. In the evolutionary process, our algorithm searches the solution space of the problem through a mixture crossover of feasible and infeasible...Show More
In this paper, we propose a generic, two-phase framework for solving constrained optimization problems using genetic algorithms. In the first phase of the algorithm, the objective function is completely disregarded and the constrained optimization problem is treated as a constraint satisfaction problem. The genetic search is directed toward minimizing the constraint violation of the solutions and ...Show More
The sequence of manufacturing process should meet process constraints during the process planning optimization. The qualitative constraints are represented implicitly as geometry relationships, process rules and manufacturing environment, it is difficult to apply in the process optimization. A constraint matrix is established to express process constraints; transfer rules and regulation are also d...Show More
A learning paradigm is proposed and investigated, in which the classical framework of learning from examples is enhanced by the introduction of hard pointwise constraints, i.e., constraints imposed on a finite set of examples that cannot be violated. Such constraints arise, e.g., when requiring coherent decisions of classifiers acting on different views of the same pattern. The classical examples ...Show More
We investigate a model for 2 dimensional (2D) input constraints. The model is an analogy of a model for 1 dimensional input constraints. We show that the model for 2D constraints is complete for 2D (d,k) Run-Length-Limited(RLL) constraints for positive integers d and k with 2d < k and that the model is incomplete for 2D (2, 4) RLL constraints.Show More
This paper proposes a new kind of constraint handling method for optimization. In the proposed method, a transformation of constraint functions to be another objective function will be discussed in details. With this technique we can transform the constraint problem to be unconstraint multiobjective optimization and use the multi-objective optimization methods to find feasible solutions. The perfo...Show More
Consider a downlink communication system where multiantenna base stations transmit independent data streams to decentralized single-antenna users over a common frequency band. The goal of the base stations is to jointly adjust the beamforming vectors to minimize the transmission powers while ensuring the signal-to-interference-noise ratio requirement of each user within the system. At the same tim...Show More
Interference cancellation or multiuser detection schemes are of great interest to the CDMA community because of their potential ability to treat the near-far problem and significantly reduce interference levels. A linear adaptive multiuser detection scheme based on constrained optimization is presented, constraints are constructed from knowledge of users' codes and associated timing. Both single- ...Show More
Many practical engineering problems can be transformed into constrained optimization problems (COPs) , scholars prefer to use evolutionary algorithms (EA) to deal with COPS. Differential evolution (DE) algorithm has strong global search and convergence ability than other EA, however, when dealing with COPs, the search ability of DE algorithm is affected by parameter setting and constraint handling...Show More
During the last three decades, several constraint handling techniques have been developed to be used with evolutionary algorithms (EAs). According to the no free lunch theorem, it is impossible for a single constraint handling technique to outperform all other techniques on every problem. In other words, depending on several factors such as the ratio between feasible search space and the whole sea...Show More
In order to suppress mass low power jamming and some high power jamming in GPS receiver; GPS planar array combined sidelobe constraints and quadratic null constraints is designed; sidelobe constraints can suppress low power jamming, quadratic null constraints can suppress high power jamming; computer simulation results show that this design can restrain concealed jamming from sidelobe at controlle...Show More
This work is focused on the Enumeration phase of Constraint Programming to solve Constraint Satisfaction Problems, an enumeration strategy is constituted by a variable selection heuristic and a value selection heuristic. A suitable definition and use of the enumeration strategy can strongly improve the resolution process. In order to select the enumeration strategies dynamically here we present a ...Show More
Hard constraints must always hold. Violations of soft constraints may be tolerable. Inconsistency-tolerant integrity checking serves to flexibly check both hard and soft constraints in a uniform manner. With an extended example for risk management, we illustrate that inconsistency-tolerant integrity checking methods are more efficient and more reliable for checking hard and soft constraints than t...Show More
In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixed-point arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filte...Show More
This work considers the problem of stabilization of nonlinear systems subject to rate constraints on the control inputs and constraints on the state and input variables in the presence of uncertainty. We first handle rate constraints within a soft constraints framework. A new robust predictive controller formulation that minimizes rate constraint violation while guaranteeing stabilization and stat...Show More
Encoding to SAT and applying a state-of-the-art SAT solver can be a highly effective way of solving constraint problems. For many types of constraints there exist several alternative SAT encodings; and the choice of encoding can significantly affect SAT solver performance for any given problem. Previous work has shown that machine learning (ML) can be used to select SAT encodings for some constrai...Show More
In this paper, we consider the design of finite-impulse response variable digital filters (VDFs) with variable cutoff frequency or variable fractional delay. We propose the design of VDFs with minimum integral squared error and constraints on the maximum error deviation in conjunction with flatness group delay specification or phase constraints. These specifications allow the VDFs to have approxim...Show More