Metaheuristic optimization of power and energy systems: underlying principles and main issues of the 'rush to heuristics'

• URL: http://arxiv.org/abs/2008.07491v1
• Date: Mon, 17 Aug 2020 17:33:51 GMT
• Title: Metaheuristic optimization of power and energy systems: underlying principles and main issues of the 'rush to heuristics'
• Authors: Gianfranco Chicco and Andrea Mazza
• Abstract summary: This paper considers the applications to power and energy systems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the power and energy systems area, a progressive increase of literature contributions containing applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods based on weak comparisons. This 'rush to heuristics' does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter, but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems, and aims at providing a comprehensive view of the main issues concerning the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls found in literature contributions are identified, and specific guidelines are provided on how to prepare sound contributions on the application of metaheuristic algorithms to specific problems.

Related papers

• Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection [25.29451529910051]
  We propose the first provable guarantee for algorithm selection based on algorithm features. We analyze the benefits and costs associated with algorithm features and investigate how the generalization error is affected by different factors.
  arXiv  Detail & Related papers  (2024-05-18T17:38:25Z)
• Multivariate Systemic Risk Measures and Computation by Deep Learning Algorithms [63.03966552670014]
  We discuss the key related theoretical aspects, with a particular focus on the fairness properties of primal optima and associated risk allocations. The algorithms we provide allow for learning primals, optima for the dual representation and corresponding fair risk allocations.
  arXiv  Detail & Related papers  (2023-02-02T22:16:49Z)
• Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics [89.24951036534168]
  Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model.
  arXiv  Detail & Related papers  (2022-10-23T22:21:10Z)
• Towards Target Sequential Rules [52.4562332499155]
  We propose an efficient algorithm, called targeted sequential rule mining (TaSRM) It is shown that the novel algorithm TaSRM and its variants can achieve better experimental performance compared to the existing baseline algorithm.
  arXiv  Detail & Related papers  (2022-06-09T18:59:54Z)
• Instance-Dependent Confidence and Early Stopping for Reinforcement Learning [99.57168572237421]
  Various algorithms for reinforcement learning (RL) exhibit dramatic variation in their convergence rates as a function of problem structure. This research provides guarantees that explain textitex post the performance differences observed. A natural next step is to convert these theoretical guarantees into guidelines that are useful in practice.
  arXiv  Detail & Related papers  (2022-01-21T04:25:35Z)
• A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning [6.003234406806134]
  In this work, we propose the Dirichlet policy for continuous allocation tasks and analyze the bias and variance of its policy gradients. We demonstrate that the Dirichlet policy is bias-free and provides significantly faster convergence and better performance than the Gaussian-softmax policy. The experimental results show the potential to prescribe optimal operation, improve the efficiency and sustainability of multi-power source systems.
  arXiv  Detail & Related papers  (2022-01-20T20:41:04Z)
• 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)
• Adaptive Discretization in Online Reinforcement Learning [9.560980936110234]
  Two major questions in designing discretization-based algorithms are how to create the discretization and when to refine it. We provide a unified theoretical analysis of tree-based hierarchical partitioning methods for online reinforcement learning. Our algorithms are easily adapted to operating constraints, and our theory provides explicit bounds across each of the three facets.
  arXiv  Detail & Related papers  (2021-10-29T15:06:15Z)
• Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms [71.62575565990502]
  We prove that the generalization error of an optimization algorithm can be bounded on the complexity' of the fractal structure that underlies its generalization measure. We further specialize our results to specific problems (e.g., linear/logistic regression, one hidden/layered neural networks) and algorithms.
  arXiv  Detail & Related papers  (2021-06-09T08:05:36Z)
• Causal Policy Gradients [6.123324869194195]
  Causal policy gradients (CPGs) provide a common framework for analysing key state-of-the-art algorithms. CPGs are shown to generalise traditional policy gradients, and yield a principled way of incorporating prior knowledge of a problem domain's generative processes.
  arXiv  Detail & Related papers  (2021-02-20T14:51:12Z)
• Maximal Algorithmic Caliber and Algorithmic Causal Network Inference: General Principles of Real-World General Intelligence? [0.0]
  Ideas and formalisms from far-from-equilibrium thermodynamics are ported to the context of computational processes. A Principle of Maximumic Caliber is proposed, providing guidance as to what computational processes one should hypothesize.
  arXiv  Detail & Related papers  (2020-05-10T06:14:59Z)

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.