Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments

• URL: http://arxiv.org/abs/2012.07225v2
• Date: Fri, 25 Dec 2020 13:35:04 GMT
• Title: Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments
• Authors: Cuie Yang, Jinliang Ding, Yaochu Jin, Tianyou Chai
• Abstract summary: This paper proposes a data-driven optimization algorithm to deal with the challenges presented by the dynamic environments. First, a data stream ensemble learning method is adopted to train the surrogates so that each base learner of the ensemble learns the time-varying objective function in the previous environments. After that, a multi-task evolutionary algorithm is employed to simultaneously optimize the problems in the past environments assisted by the ensemble surrogate.
• Score: 26.93254582875251
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing work on data-driven optimization focuses on problems in static environments, but little attention has been paid to problems in dynamic environments. This paper proposes a data-driven optimization algorithm to deal with the challenges presented by the dynamic environments. First, a data stream ensemble learning method is adopted to train the surrogates so that each base learner of the ensemble learns the time-varying objective function in the previous environments. After that, a multi-task evolutionary algorithm is employed to simultaneously optimize the problems in the past environments assisted by the ensemble surrogate. This way, the optimization tasks in the previous environments can be used to accelerate the tracking of the optimum in the current environment. Since the real fitness function is not available for verifying the surrogates in offline data-driven optimization, a support vector domain description that was designed for outlier detection is introduced to select a reliable solution. Empirical results on six dynamic optimization benchmark problems demonstrate the effectiveness of the proposed algorithm compared with four state-of-the-art data-driven optimization algorithms.

Related papers

• Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning [32.41025515064283]
  We propose a simple yet effective meta-learning-based optimization framework for solving expensive dynamic optimization problems. This framework is flexible, allowing any off-the-shelf continuously differentiable surrogate model to be used in a plug-in manner. Experiments demonstrate the effectiveness of the proposed algorithm framework compared to several state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2023-10-19T07:42:51Z)
• Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver. This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
  arXiv  Detail & Related papers  (2023-01-28T01:50:42Z)
• A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments [9.098403098464704]
  Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach for tackling expensive black-box optimization problems. This paper proposes a simple but effective transfer learning framework to empower data-driven evolutionary optimization to solve dynamic optimization problems. Experiments on synthetic benchmark test problems and a real-world case study demonstrate the effectiveness of our proposed algorithm.
  arXiv  Detail & Related papers  (2022-11-05T11:19:50Z)
• Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization [3.012067935276772]
  Generalized data-driven evolutionary algorithm (GDDE) is proposed to reduce the number of simulation runs on well-placement and control optimization problems. Probabilistic neural network (PNN) is adopted as the classifier to select informative and promising candidates.
  arXiv  Detail & Related papers  (2022-06-07T09:07:49Z)
• An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization [19.336520152294213]
  We propose a novel prediction algorithm based on incremental support vector machine (ISVM) We treat the solving of dynamic multiobjective optimization problems (DMOPs) as an online learning process. The proposed algorithm can effectively tackle dynamic multiobjective optimization problems.
  arXiv  Detail & Related papers  (2021-02-24T08:51:23Z)
• Automatically Learning Compact Quality-aware Surrogates for Optimization Problems [55.94450542785096]
  Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values. Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making. We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
  arXiv  Detail & Related papers  (2020-06-18T19:11:54Z)
• Tiering as a Stochastic Submodular Optimization Problem [5.659969270836789]
  Tiering is an essential technique for building large-scale information retrieval systems. We show that the optimal tiering as an optimization problem can be cast as a submodular minimization problem with a submodular knapsack constraint.
  arXiv  Detail & Related papers  (2020-05-16T07:39:29Z)
• Self-Directed Online Machine Learning for Topology Optimization [58.920693413667216]
  Self-directed Online Learning Optimization integrates Deep Neural Network (DNN) with Finite Element Method (FEM) calculations. Our algorithm was tested by four types of problems including compliance minimization, fluid-structure optimization, heat transfer enhancement and truss optimization. It reduced the computational time by 2 5 orders of magnitude compared with directly using methods, and outperformed all state-of-the-art algorithms tested in our experiments.
  arXiv  Detail & Related papers  (2020-02-04T20:00:28Z)
• Bilevel Optimization for Differentially Private Optimization in Energy Systems [53.806512366696275]
  This paper studies how to apply differential privacy to constrained optimization problems whose inputs are sensitive. The paper shows that, under a natural assumption, a bilevel model can be solved efficiently for large-scale nonlinear optimization problems.
  arXiv  Detail & Related papers  (2020-01-26T20:15:28Z)
• 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)
• Model Inversion Networks for Model-Based Optimization [110.24531801773392]
  We propose model inversion networks (MINs), which learn an inverse mapping from scores to inputs. MINs can scale to high-dimensional input spaces and leverage offline logged data for both contextual and non-contextual optimization problems. We evaluate MINs on tasks from the Bayesian optimization literature, high-dimensional model-based optimization problems over images and protein designs, and contextual bandit optimization from logged data.
  arXiv  Detail & Related papers  (2019-12-31T18:06:49Z)

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.