Dynamic communication topologies for distributed heuristics in energy
system optimization algorithms
- URL: http://arxiv.org/abs/2108.01380v1
- Date: Tue, 3 Aug 2021 09:30:56 GMT
- Title: Dynamic communication topologies for distributed heuristics in energy
system optimization algorithms
- Authors: Stefanie Holly and Astrid Nie{\ss}e
- Abstract summary: We present an approach for adapting the communication topology during runtime.
We compare the approach to common static topologies regarding the performance of an exemplary distributed optimization.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The communication topology is an essential aspect in designing distributed
optimization heuristics. It can influence the exploration and exploitation of
the search space and thus the optimization performance in terms of solution
quality, convergence speed and collaboration costs, all relevant aspects for
applications operating critical infrastructure in energy systems. In this work,
we present an approach for adapting the communication topology during runtime,
based on the principles of simulated annealing. We compare the approach to
common static topologies regarding the performance of an exemplary distributed
optimization heuristic. Finally, we investigate the correlations between
fitness landscape properties and defined performance metrics.
Related papers
- Enhancing Explainability and Reliable Decision-Making in Particle Swarm Optimization through Communication Topologies [14.88267665338613]
This study focuses on how different communication topologies affect convergence and search behaviors.
Using an adapted IOHxplainer, we investigate how these topologies influence information flow, diversity, and convergence speed.
arXiv Detail & Related papers (2025-04-17T10:05:10Z) - Optimal Control Operator Perspective and a Neural Adaptive Spectral Method [43.684201849848314]
Optimal control problems (OCPs) involve finding a control function for a dynamical system such that a cost functional is optimized.
We propose a novel instance-solution control operator perspective, which solves OCPs in a one-shot manner.
Experiments on synthetic environments and a real-world dataset verify the effectiveness and efficiency of our approach.
arXiv Detail & Related papers (2024-12-17T02:06:34Z) - Co-Optimization of Environment and Policies for Decentralized Multi-Agent Navigation [14.533605727697775]
This work views the multi-agent system and its surrounding environment as a co-evolving system, where the behavior of one affects the other.
We develop an algorithm that alternates between sub-objectives to search for an optimal of agent actions and obstacle configurations in the environment.
arXiv Detail & Related papers (2024-03-21T17:37:43Z) - Hallmarks of Optimization Trajectories in Neural Networks: Directional Exploration and Redundancy [75.15685966213832]
We analyze the rich directional structure of optimization trajectories represented by their pointwise parameters.
We show that training only scalar batchnorm parameters some while into training matches the performance of training the entire network.
arXiv Detail & Related papers (2024-03-12T07:32:47Z) - 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) - Federated Conditional Stochastic Optimization [110.513884892319]
Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML.
This paper proposes algorithms for distributed federated learning.
arXiv Detail & Related papers (2023-10-04T01:47:37Z) - On the Computation-Communication Trade-Off with A Flexible Gradient
Tracking Approach [6.877328172726638]
We propose a flexible gradient tracking approach with adjustable computation and communication steps for solving distributed optimization problem over networks.
We derive both the computation and communication complexities for achieving arbitrary accuracy on smooth and strongly convex objective functions.
arXiv Detail & Related papers (2023-06-12T14:46:21Z) - 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) - Improvement of Computational Performance of Evolutionary AutoML in a
Heterogeneous Environment [0.0]
We propose a modular approach to increase the quality of evolutionary optimization for modelling pipelines with a graph-based structure.
The implemented algorithms are available as a part of the open-source framework FEDOT.
arXiv Detail & Related papers (2023-01-12T15:59:04Z) - Optimization of Rocker-Bogie Mechanism using Heuristic Approaches [0.0]
This paper focuses on the Rocker Bogie mechanism, a standard suspension methodology associated with foreign terrains.
This paper presents extensive tests on Simulated Annealing, Genetic Algorithms, Swarm Intelligence techniques, Basin Hoping and Differential Evolution.
arXiv Detail & Related papers (2022-09-14T21:02:01Z) - Multi-Objective Constrained Optimization for Energy Applications via
Tree Ensembles [55.23285485923913]
Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives.
In some cases, proposed optimal solutions need to obey explicit input constraints related to physical properties or safety-critical operating conditions.
This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems.
arXiv Detail & Related papers (2021-11-04T20:18:55Z) - A Field Guide to Federated Optimization [161.3779046812383]
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data.
This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms.
arXiv Detail & Related papers (2021-07-14T18:09:08Z) - Robust and Efficient Swarm Communication Topologies for Hostile
Environments [0.4588028371034406]
We present a study of the impact of loss of agents on the performance of such algorithms as a function of the initial network configuration.
The results reveal interesting trade-offs between efficiency, robustness, and performance for different topologies.
arXiv Detail & Related papers (2020-08-21T16:38:35Z)
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.