scholarly journals Robustness and Stability in Constraint Programming under Dynamism and Uncertainty

2014 ◽  
Vol 49 ◽  
pp. 49-78 ◽  
Author(s):  
L. Climent ◽  
R. J. Wallace ◽  
M. A. Salido ◽  
F. Barber
Keyword(s):  
Constraint Programming ◽  
Original Problem ◽  
Search Algorithm ◽  
Real Life ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
General Purpose ◽  
Constraint Satisfaction Problems ◽  
Life Problems ◽  
Random Instances

Many real life problems that can be solved by constraint programming, come from uncertain and dynamic environments. Because of the dynamism, the original problem may change over time, and thus the solution found for the original problem may become invalid. For this reason, dealing with such problems has become an important issue in the fields of constraint programming. In some cases, there exist extant knowledge about the uncertain and dynamic environment. In other cases, this information is fragmentary or unknown. In this paper, we extend the concept of robustness and stability for Constraint Satisfaction Problems (CSPs) with ordered domains, where only limited assumptions need to be made as to possible changes. We present a search algorithm that searches for both robust and stable solutions for CSPs of this nature. It is well-known that meeting both criteria simultaneously is a desirable objective for constraint solving in uncertain and dynamic environments. We also present compelling evidence that our search algorithm outperforms other general-purpose algorithms for dynamic CSPs using random instances and benchmarks derived from real life problems.

scholarly journals A fine-grained arc-consistency algorithm for non-normalized constraint satisfaction problems

2011 ◽  
Vol 21 (4) ◽  
pp. 733-744 ◽  
Author(s):  
Marlene Arangú ◽  
Miguel Salido
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Programming ◽  
Real Life ◽  
Search Space ◽  
Constraint Satisfaction Problems ◽  
Fine Grained ◽  
Arc Consistency ◽  
Life Problems ◽  
Programming Techniques ◽  
Np Complete

A fine-grained arc-consistency algorithm for non-normalized constraint satisfaction problems Constraint programming is a powerful software technology for solving numerous real-life problems. Many of these problems can be modeled as Constraint Satisfaction Problems (CSPs) and solved using constraint programming techniques. However, solving a CSP is NP-complete so filtering techniques to reduce the search space are still necessary. Arc-consistency algorithms are widely used to prune the search space. The concept of arc-consistency is bidirectional, i.e., it must be ensured in both directions of the constraint (direct and inverse constraints). Two of the most well-known and frequently used arc-consistency algorithms for filtering CSPs are AC3 and AC4. These algorithms repeatedly carry out revisions and require support checks for identifying and deleting all unsupported values from the domains. Nevertheless, many revisions are ineffective, i.e., they cannot delete any value and consume a lot of checks and time. In this paper, we present AC4-OP, an optimized version of AC4 that manages the binary and non-normalized constraints in only one direction, storing the inverse founded supports for their later evaluation. Thus, it reduces the propagation phase avoiding unnecessary or ineffective checking. The use of AC4-OP reduces the number of constraint checks by 50% while pruning the same search space as AC4. The evaluation section shows the improvement of AC4-OP over AC4, AC6 and AC7 in random and non-normalized instances.

Building Search Methods with Self-Confidence in a Constraint Programming Library

2018 ◽  
Vol 27 (04) ◽  
pp. 1860003
Author(s):  
Nikolaos Pothitos ◽  
Panagiotis Stamatopoulos
Keyword(s):  
Constraint Satisfaction ◽  
Real Life ◽  
Constraint Satisfaction Problems ◽  
Search Method ◽  
Search Methods ◽  
Self Confidence ◽  
Programming Library ◽  
Life Problems ◽  
Constrained Programming ◽  
Fine Tune

In the late 1990s, Constrained Programming (CP) promised to separate the declaration of a problem from the process to solve it. This work attempts to serve this direction, by implementing and presenting a modular way to define search methods that seek solutions to arbitrary Constraint Satisfaction Problems (CSPs). The user just declares their CSP, and it can be solved using a portfolio of search methods already in place. Apart from the pluggable search methods framework for any CSP, we also introduce pluggable heuristics for our search methods. We found an efficient stochastic heuristics’ paradigm that smoothly combines randomness with normal heuristics. We consider a factor of disobedience to normal heuristics, and we fine-tune it each time, according to our estimation of normal heuristics’ reliability (confidence). We prove mathematically that while the disobedience factor decreases, the stochastic heuristics approximate deterministic normal heuristics. Our algebraic evidence is supported by empirical evaluations on real life problems: A new search method, namely PoPS, that exploits this heuristics’ paradigm, can outperform regular well-known constructive search methods.

scholarly journals TMsim: An Algorithmic Tool for the Parametric and Worst-Case Simulation of Systems with Uncertainties

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Riccardo Trinchero ◽  
Paolo Manfredi ◽  
Igor S. Stievano
Keyword(s):  
Real Life ◽  
Theoretical Background ◽  
General Purpose ◽  
Worst Case Analysis ◽  
Worst Case ◽  
Uncertain Variables ◽  
Quarter Car Model ◽  
Taylor Models ◽  
Life Problems ◽  
Linear Equalizer

This paper presents a general purpose, algebraic tool—named TMsim—for the combined parametric and worst-case analysis of systems with bounded uncertain parameters. The tool is based on the theory of Taylor models and represents uncertain variables on a bounded domain in terms of a Taylor polynomial plus an interval remainder accounting for truncation and round-off errors. This representation is propagated from inputs to outputs by means of a suitable redefinition of the involved calculations, in both scalar and matrix form. The polynomial provides a parametric approximation of the variable, while the remainder gives a conservative bound of the associated error. The combination between the bound of the polynomial and the interval remainder provides an estimation of the overall (worst-case) bound of the variable. After a preliminary theoretical background, the tool (freely available online) is introduced step by step along with the necessary theoretical notions. As a validation, it is applied to illustrative examples as well as to real-life problems of relevance in electrical engineering applications, specifically a quarter-car model and a continuous-time linear equalizer.

A FRAMEWORK FOR INTEGRATING ARTIFICIAL NEURAL NETWORKS AND LOGIC PROGRAMMING

1995 ◽  
Vol 04 (01n02) ◽  
pp. 3-32 ◽  
Author(s):  
J.H.M. LEE ◽  
V.W.L. TAM
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Logic Programming ◽  
Operational Semantics ◽  
Real Life ◽  
Constraint Logic Programming ◽  
Constraint Satisfaction Problems ◽  
Problem Size ◽  
Life Problems ◽  
Artificial Neural

Many real-life problems belong to the class of constraint satisfaction problems (CSP’s), which are NP-complete, and some NP-hard, in general. When the problem size grows, it becomes difficult to program solutions and to execute the solution in a timely manner. In this paper, we present a general framework for integrating artificial neural networks and logic programming so as to provide an efficient and yet expressive programming environment for solving CSP’s. To realize this framework, we propose PROCLANN, a novel constraint logic programming language. The PROCLANN language retains the simple and elegant declarative semantics of constraint logic programming. Operationally, PROCLANN uses the standard goal reduction strategy in the frontend to generate constraints, and an efficient backend constraint-solver based on artificial neural networks. Its operational semantics is probabilistic in nature. We show that PROCLANN is sound and weakly complete. A novelty of PROCLANN is that while it is a committed-choice language, PROCLANN supports non-determinism, allowing the generation of multiple answers to a query. An initial prototype implementation of PROCLANN is constructed and demonstrates empirically that PROCLANN out-performs the state of art in constraint logic programming implementation on certain hard instances of CSP’s.

A novel hybrid water wave optimization algorithm for solving complex constrained engineering problems

2021 ◽  
Vol 63 (6) ◽  
pp. 560-564
Author(s):  
Dildar Gürses ◽  
Nantiwat Pholdee ◽  
Sujin Bureerat ◽  
Sadiq M. Sait ◽  
Ali Rıza Yıldız
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Water Wave ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Real Life ◽  
Processing Parameters ◽  
Parameters Optimization ◽  
Optimization Approach ◽  
Life Problems

Abstract In this work, a new hybrid optimization algorithm (HWW-NM), which combines the Nelder-Mead local search algorithm with the water wave algorithm, is introduced to solve real-world engineering optimization problems. This paper is one of the first studies in which both the water wave algorithm and the HWW-NM are applied to processing parameters optimization for manufacturing processes. HWW-NM performance is measured using the cantilever beam problem. Additionally, a problem for milling manufacturing optimization is posed and solved to evaluate HWW-NM performance in real-world applications. The results reveal that HWW-NM is an attractive optimization approach for optimizing real-life problems.

Evaluation of EHO Algorithm’s in solving various real-life problems

2020 ◽  
Vol 1 (2) ◽  
pp. 46-60
Author(s):  
Ahmed R. Ridwan
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Real Life ◽  
Service Selection ◽  
Benchmark Problems ◽  
Herding Behavior ◽  
Life Problems ◽  
Metaheuristic Search ◽  
Pid Controller Tuning ◽  
Hard Problems

Abstract:  This paper, presents a new swarm-based metaheuristic search algorithm, known as Elephant Herding Optimization (EHO), which is proposed for solving various daily real-life problems such as benchmark problems, Service Selection in QoS-Aware Web Service Composition, Energy-Based Localization, PID controller tuning, Appliance Scheduling in Smart Grid identification and other problems. The EHO method is inspired by the herding behavior of elephant group. In nature, elephants live in clans under the leadership of a matriarch (female elephant), while the male elephants separate from their family when they grow up. These two behaviors are used by EHO as two operators: clan updating operator and separating operator, which will be used in the optimizing process. Elephant Herding Optimization (EHO) is used to solve NP-Hard problems.

Comparison of nonlinear solvers within continuation method for steady-state variably saturated groundwater flow modelling

2021 ◽  
Vol 36 (4) ◽  
pp. 183-195
Author(s):  
Denis Anuprienko
Keyword(s):  
Steady State ◽  
Line Search ◽  
Search Algorithm ◽  
Continuation Method ◽  
Real Life ◽  
Richards Equation ◽  
Life Problems ◽  
Nonlinear Solvers ◽  
Groundwater Flow Modelling ◽  
Search Approach

Abstract Nonlinearity continuation method, applied to boundary value problems for steady-state Richards equation, gradually approaches the solution through a series of intermediate problems. Originally, the Newton method with simple line search algorithm was used to solve the intermediate problems. In the present paper, other solvers such as Picard and mixed Picard–Newton methods are considered, combined with slightly modified line search approach. Numerical experiments are performed with advanced finite volume discretizations for model and real-life problems.

The Psychologist in the Medical School: An Example of Solving Real-Life Problems

1970 ◽  
Author(s):  
Matisyohu Weisenberg ◽  
Carl Eisdorfer ◽  
C. Richard Fletcher ◽  
Murray Wexler
Keyword(s):  
Medical School ◽  
Real Life ◽  
Life Problems

scholarly journals AnANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

2021 ◽  
Vol 11 (11) ◽  
pp. 4757
Author(s):  
Aleksandra Bączkiewicz ◽  
Jarosław Wątróbski ◽  
Wojciech Sałabun ◽  
Joanna Kołodziejczyk
Keyword(s):  
Atmospheric Pressure ◽  
Real Life ◽  
Real Data ◽  
Weather Conditions ◽  
Maximum Temperature ◽  
Air Masses ◽  
Weather Forecasts ◽  
Life Problems ◽  
The World ◽  
The City

Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature, especially weather indicators, has been widely discussed. However, the various areas of the world differ in terms of their difficulty and ability in preparing accurate weather forecasts. Poland lies in a zone with a moderate transition climate, which is characterized by seasonality and the inflow of many types of air masses from different directions, which, combined with the compound terrain, causes climate variability and makes it difficult to accurately predict the weather. For this reason, it is necessary to adapt the model to the prediction of weather conditions and verify its effectiveness on real data. The principal aim of this study is to present the use of a regressive model based on a unidirectional multilayer neural network, also called a Multilayer Perceptron (MLP), to predict selected weather indicators for the city of Szczecin in Poland. The forecast of the model we implemented was effective in determining the daily parameters at 96% compliance with the actual measurements for the prediction of the minimum and maximum temperature for the next day and 83.27% for the prediction of atmospheric pressure.

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