A Hybrid APM-CPGSO Approach for Constraint Satisfaction Problem Solving: Application to Remote Sensing

• URL: http://arxiv.org/abs/2106.05193v1
• Date: Sun, 6 Jun 2021 22:05:22 GMT
• Title: A Hybrid APM-CPGSO Approach for Constraint Satisfaction Problem Solving: Application to Remote Sensing
• Authors: Zouhayra Ayadi, Wadii Boulila, Imed Riadh Farah
• Abstract summary: Constraint satisfaction problem (CSP) has been actively used for modeling and solving a wide range of complex real-world problems. Existing complete methods for problem-solving are in most cases unsuitable. This paper proposes a novel approach that combines incomplete and complete CSP methods for problem-solving.
• Score: 2.6641834518599308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Constraint satisfaction problem (CSP) has been actively used for modeling and solving a wide range of complex real-world problems. However, it has been proven that developing efficient methods for solving CSP, especially for large problems, is very difficult and challenging. Existing complete methods for problem-solving are in most cases unsuitable. Therefore, proposing hybrid CSP-based methods for problem-solving has been of increasing interest in the last decades. This paper aims at proposing a novel approach that combines incomplete and complete CSP methods for problem-solving. The proposed approach takes advantage of the group search algorithm (GSO) and the constraint propagation (CP) methods to solve problems related to the remote sensing field. To the best of our knowledge, this paper represents the first study that proposes a hybridization between an improved version of GSO and CP in the resolution of complex constraint-based problems. Experiments have been conducted for the resolution of object recognition problems in satellite images. Results show good performances in terms of convergence and running time of the proposed CSP-based method compared to existing state-of-the-art methods.

Related papers

• CHARME: A chain-based reinforcement learning approach for the minor embedding problem [16.24890195949869]
  We propose a novel approach utilizing Reinforcement Learning (RL) techniques to address the minor embedding problem, named CHARME. CHARME includes three key components: a Graph Neural Network (GNN) architecture for policy modeling, a state transition algorithm ensuring solution validity, and an order exploration strategy for effective training. In details, CHARME yields superior solutions compared to fast embedding methods such as Minorminer and ATOM.
  arXiv  Detail & Related papers  (2024-06-11T10:12:10Z)
• Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods [52.0617030129699]
  We introduce a novel theoretical framework for analyzing the effectiveness of DeepMatching Networks and Reinforcement Learning methods. Our main contribution holds for a broad class of problems including Max-and Min-Cut, Max-$k$-Bipartite-Bi, Maximum-Weight-Bipartite-Bi, and Traveling Salesman Problem. As a byproduct of our analysis we introduce a novel regularization process over vanilla descent and provide theoretical and experimental evidence that it helps address vanishing-gradient issues and escape bad stationary points.
  arXiv  Detail & Related papers  (2023-10-08T23:39:38Z)
• Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models [62.96551299003463]
  We propose textbftextitThought Propagation (TP) to enhance the complex reasoning ability of Large Language Models. TP first prompts LLMs to propose and solve a set of analogous problems that are related to the input one. TP reuses the results of analogous problems to directly yield a new solution or derive a knowledge-intensive plan for execution to amend the initial solution obtained from scratch.
  arXiv  Detail & Related papers  (2023-10-06T01:40:09Z)
• Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control Approach [0.3093890460224435]
  We address the solution of the popular Wordle puzzle, using new reinforcement learning methods. For the Wordle puzzle, they yield on-line solution strategies that are very close to optimal at relatively modest computational cost.
  arXiv  Detail & Related papers  (2022-11-15T03:46:41Z)
• Divide-and-conquer embedding for QUBO quantum annealing [0.0]
  We show that a problem-focused approach to embedding can improve performance by orders of magnitude. Our results show that a problem-focused approach to embedding can improve performance by orders of magnitude.
  arXiv  Detail & Related papers  (2022-11-03T23:22:06Z)
• ARES: An Efficient Algorithm with Recurrent Evaluation and Sampling-Driven Inference for Maximum Independent Set [48.57120672468062]
  This paper introduces an efficient algorithm for the Maximum Independent Set (MIS) problem, incorporating two innovative techniques. The proposed algorithm outperforms state-of-the-art algorithms in terms of solution quality, computational efficiency, and stability.
  arXiv  Detail & Related papers  (2022-08-16T14:39:38Z)
• A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems [54.61091936472494]
  We develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. Results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems.
  arXiv  Detail & Related papers  (2021-03-10T03:16:12Z)
• Improving Constraint Satisfaction Algorithm Efficiency for the AllDifferent Constraint [0.0]
  Combinatorial problems stated as Constraint Satisfaction Problems (CSP) are examined. It is shown that any algorithm designed for the original CSP, and involving the AllDifferent constraint, has at least the same level of efficacy when simultaneously applied to both the original and its complementary problem.
  arXiv  Detail & Related papers  (2020-12-07T12:14:55Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)
• MineReduce: an approach based on data mining for problem size reduction [58.720142291102135]
  This paper presents an approach named MineReduce, which uses mined patterns to perform problem size reduction. We present an application of MineReduce to improve a for the heterogeneous fleet vehicle routing problem.
  arXiv  Detail & Related papers  (2020-05-15T08:49:50Z)
• GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction [69.94831587339539]
  We present a modified Cross-Entropy Method (CEM) that uses a masked auto-regressive neural network for modeling uniform distributions over the solution space. Our algorithm is able to express complicated solution spaces, thus allowing it to track a variety of different solution regions.
  arXiv  Detail & Related papers  (2020-02-17T20:21:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.