An enhanced simulation-based iterated local search metaheuristic for gravity fed water distribution network design optimization

• URL: http://arxiv.org/abs/2009.01197v3
• Date: Mon, 7 Jun 2021 10:38:52 GMT
• Title: An enhanced simulation-based iterated local search metaheuristic for gravity fed water distribution network design optimization
• Authors: Willian C. S. Martinho, Rafael A. Melo, Kenneth S\"orensen
• Abstract summary: The gravity fed water distribution network design (WDND) optimization problem consists in determining the pipe diameters of a water network. We propose a new simulation-based iterated local search metaheuristic which further explores the structure of the problem in an attempt to obtain high quality solutions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The gravity fed water distribution network design (WDND) optimization problem consists in determining the pipe diameters of a water network such that hydraulic constraints are satisfied and the total cost is minimized. Traditionally, such design decisions are made on the basis of expert experience. When networks increase in size, however, rules of thumb will rarely lead to near optimal decisions. Over the past thirty years, a large number of techniques have been developed to tackle the problem of optimally designing a water distribution network. In this paper, we tackle the NP-hard water distribution network design (WDND) optimization problem in a multi-period setting where time varying demand patterns occur. We propose a new simulation-based iterated local search metaheuristic which further explores the structure of the problem in an attempt to obtain high quality solutions. Computational experiments show that our approach is very competitive as it is able to improve over a state-of-the-art metaheuristic for most of the performed tests. Furthermore, it converges much faster to low cost solutions and demonstrates a more robust performance in that it obtains smaller deviations from the best known solutions.

Related papers

• Evolutionary Greedy Algorithm for Optimal Sensor Placement Problem in Urban Sewage Surveillance [6.133464220178637]
  We propose a novel evolutionary greedy algorithm (EG) to enable efficient and effective optimization for large-scale directed networks. The proposed model is evaluated on both small-scale synthetic networks and a large-scale, real-world sewage network in Hong Kong.
  arXiv  Detail & Related papers  (2024-09-25T09:27:51Z)
• Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme [14.120671138290104]
  We employ a rational neural network to accurately estimate the local smoothness of the solution. This approach achieves a granular reconstruction with significantly reduced dissipation. We demonstrate the effectiveness of our approach on several one-, two-, and three-dimensional fluid flow problems.
  arXiv  Detail & Related papers  (2024-09-13T22:11:03Z)
• DiffSG: A Generative Solver for Network Optimization with Diffusion Model [75.27274046562806]
  Diffusion generative models can consider a broader range of solutions and exhibit stronger generalization by learning parameters. We propose a new framework, which leverages intrinsic distribution learning of diffusion generative models to learn high-quality solutions.
  arXiv  Detail & Related papers  (2024-08-13T07:56:21Z)
• DiffuSolve: Diffusion-based Solver for Non-convex Trajectory Optimization [9.28162057044835]
  Optimal trajectory local is computationally expensive for nonlinear and high-dimensional dynamical systems. In this paper we introduce Diffu-based general model for non-dimensional optima problems. We also present Diff+, a novel constrained diffusion model with an additional loss in that further reduces the problem violations.
  arXiv  Detail & Related papers  (2024-02-22T03:52:17Z)
• Federated Multi-Level Optimization over Decentralized Networks [55.776919718214224]
  We study the problem of distributed multi-level optimization over a network, where agents can only communicate with their immediate neighbors. We propose a novel gossip-based distributed multi-level optimization algorithm that enables networked agents to solve optimization problems at different levels in a single timescale. Our algorithm achieves optimal sample complexity, scaling linearly with the network size, and demonstrates state-of-the-art performance on various applications.
  arXiv  Detail & Related papers  (2023-10-10T00:21:10Z)
• Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables [0.3626013617212667]
  A new Quality-Diversity methodology based on mixed variables is proposed in the context of limited simulation budget. The proposed approach provides valuable trade-offs for decision-markers for complex system design.
  arXiv  Detail & Related papers  (2023-09-11T14:29:47Z)
• RDRN: Recursively Defined Residual Network for Image Super-Resolution [58.64907136562178]
  Deep convolutional neural networks (CNNs) have obtained remarkable performance in single image super-resolution. We propose a novel network architecture which utilizes attention blocks efficiently.
  arXiv  Detail & Related papers  (2022-11-17T11:06:29Z)
• DDCNet: Deep Dilated Convolutional Neural Network for Dense Prediction [0.0]
  A receptive field (ERF) and a higher resolution of spatial features within a network are essential for providing higher-resolution dense estimates. We present a systemic approach to design network architectures that can provide a larger receptive field while maintaining a higher spatial feature resolution.
  arXiv  Detail & Related papers  (2021-07-09T23:15:34Z)
• 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)
• Rethinking Differentiable Search for Mixed-Precision Neural Networks [83.55785779504868]
  Low-precision networks with weights and activations quantized to low bit-width are widely used to accelerate inference on edge devices. Current solutions are uniform, using identical bit-width for all filters. This fails to account for the different sensitivities of different filters and is suboptimal. Mixed-precision networks address this problem, by tuning the bit-width to individual filter requirements.
  arXiv  Detail & Related papers  (2020-04-13T07:02:23Z)
• Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning [96.01176486957226]
  Resource allocation and transceivers in wireless networks are usually designed by solving optimization problems. In this article, we introduce unsupervised and reinforced-unsupervised learning frameworks for solving both variable and functional optimization problems.
  arXiv  Detail & Related papers  (2020-01-03T11:01:52Z)

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.