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Population-Level Center-Based Sampling for Meta-Heuristic algorithms

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SSRN

SSRN 2024年

作者： Khosrowshahli, Rasa Rahnamayan, Shahryar Ibrahim, Amin Bidgoli, Azam Asilian Makrehchi, Masoud Faculty of Engineering and Applied Sciences Ontario Tech University OshawaON Canada Department of Engineering Brock University St. CatharinesON Canada Faculty of Business and Information Technology Ontario Tech University OshawaON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada

In recent years, center-based sampling has demonstrated p results for enhancing the efficiency and effectiveness of meta-heuristic algorithms. The strategy of center-based sampling can be utilized at either the operation and/or population levels. Despite the overall efficiency of the center-based sampling in population-based algorithms, utilization at the operation requires customizing the strategy for a specific algorithm, degrading the generalization of the scheme. This study proposes a center-based sampling at the population level, which is operation-independent and correspondingly can be embedded in any population-based optimization algorithm. The proposed center-based sampling uses a collaborative multi-parent approach to generate multiple center-based solutions leading to augmented diversity as well as the exploitation of the solution space. Accordingly, two population-level center-based sampling, namely cluster-driven center-based sampling and ranking-driven center-based sampling are proposed to enhance population-based single- and multi-objective optimization algorithms, respectively. In order to assess the performance of proposed schemes, the proposed strategy is integrated in well-known single- and multi-objective optimization algorithms to solve the CEC-2017 benchmark functions. The experimental outcomes confirm that the algorithm with embedded population-level center-based samplings have a crucial positive impact on the convergence rate of various families of optimization algorithms compared to the parent algorithm. © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

TERIME: An improved RIME algorithm with enhanced exploration and exploitation for robust parameter extraction of photovoltaic models

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arXiv 2024年

作者： Chen, Shi-Shun Jiang, Yu-Tong Chen, Wen-Bin Li, Xiao-Yang School of Reliability and Systems Engineering Beihang University Beijing100191 China

Parameter extraction of photovoltaic (PV) models is crucial for the planning, optimization, and control of PV systems. Although some methods using meta-heuristic algorithms have been proposed to determine these parameters, the robustness of solutions obtained by these methods faces great challenges when the complexity of the PV model increases. The unstable results will affect the reliable operation and maintenance strategies of PV systems. In response to this challenge, an improved RIME algorithm with enhanced exploration and exploitation is proposed for robust and accurate parameter identification for various PV models. Specifically, the differential evolution mutation operator is integrated in the exploration phase to enhance the population diversity. Meanwhile, a new exploitation strategy incorporating randomization and neighborhood strategies simultaneously is developed to maintain the balance of exploitation width and depth. The improved RIME algorithm is applied to estimate the optimal parameters of the single-diode model (SDM), double-diode model (DDM), and triple-diode model (TDM) combined with the Lambert-W function for three PV cell and module types including RTC France, Photo Watt-PWP 201 and S75. According to the statistical analysis in 100 runs, the TEIMRE achieves more accurate and robust parameter estimations than other techniques to various PV models in varying environmental conditions. All of our source codes are publicly available at https://***/dirge1/TERIME. © 2024, CC BY.

关键词： optimization algorithms

Partition Constraints for Conjunctive Queries: Bounds and Worst-Case Optimal Joins

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arXiv 2025年

作者： Deeds, Kyle Merkl, Timo Camillo University of Washington SeattleWA United States TU Wien Vienna Austria

In the last decade, various works have used statistics on relations to improve both the theory and practice of conjunctive query execution. Starting with the AGM bound which took advantage of relation sizes, later works incorporated statistics like functional dependencies and degree constraints. Each new statistic prompted work along two lines;bounding the size of conjunctive query outputs and worst-case optimal join algorithms. In this work, we continue in this vein by introducing a new statistic called a partition constraint. This statistic captures latent structure within relations by partitioning them into sub-relations which each have much tighter degree constraints. We show that this approach can both refine existing cardinality bounds and improve existing worst-case optimal join algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

SMOP: Stochastic trust region method for multi-objective problems

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arXiv 2025年

作者： Krejić, Nataša Jerinkić, Nataša Krklec Rutešić, Luka Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad Trg Dositeja Obradovića 4 Novi Sad21000 Serbia

The problem considered is a multi-objective optimization problem, in which the goal is to find an optimal value of a vector function representing various criteria. The aim of this work is to develop an algorithm which utilizes the trust region framework with probabilistic model functions, able to cope with noisy problems, using inaccurate functions and gradients. We prove the almost sure convergence of the proposed algorithm to a Pareto critical point if the model functions are good approximations in probabilistic sense. Numerical results demonstrate effectiveness of the probabilistic trust region by comparing it to competitive stochastic multi-objective solvers. The application in supervised machine learning is showcased by training non discriminatory Logistic Regression models on different size data groups. Additionally, we use several test examples with irregularly shaped fronts to exhibit the efficiency of the algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Subgradient Method for System Identification with Non-Smooth Objectives

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arXiv 2025年

作者： Yalcin, Baturalp Lavaei, Javad University of California Berkeley United States

This paper investigates a subgradient-based algorithm to solve the system identification problem for linear time-invariant systems with non-smooth objectives. This is essential for robust system identification in safety-critical applications. While existing work provides theoretical exact recovery guarantees using optimization solvers, the design of fast learning algorithms with convergence guarantees for practical use remains unexplored. We analyze the subgradient method in this setting where the optimization problems to be solved change over time as new measurements are taken, and we establish linear convergence results for both the best and Polyak step sizes after a burn-in period. Additionally, we characterize the asymptotic convergence of the best average sub-optimality gap under diminishing and constant step sizes. Finally, we compare the time complexity of standard solvers with the subgradient algorithm and support our findings with experimental results. This is the first work to analyze subgradient algorithms for system identification with non-smooth *** Codes 62, 90, 93 © 2025, CC BY-NC-ND.

关键词： optimization algorithms

Cover-Relax-Search: A Primal Heuristic for Binary Quadratic Programs

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arXiv 2025年

作者： Huang, Weimin Isenberg, Natalie M. Drgona, Jan Vrabie, Draguna L. Dilkina, Bistra University of Southern California Los AngelesCA United States Pacific Northwest National Laboratory RichlandWA United States Johns Hopkins University BaltimoreMD United States

Binary Quadratic Programs (BQPs) are a class of NP-hard problems that arise in a wide range of applications, including finance, machine learning, and logistics. These problems are challenging to solve due to the combinatorial search space and nonlinearity. In fact, this class of optimization problems is so challenging that, in many instances, standard algorithms struggle to identify feasible solutions within a reasonable time. Primal heuristic algorithms have been developed to quickly identify feasible solutions to BQPs. In this paper, we propose Cover-Relax-Search, an efficient primal heuristic for BQPs. This approach is inspired by multiple local search algorithms, including Undercover. We evaluate the Cover-Relax-Search algorithm on multiple BQP benchmarks and show that our proposed heuristic identifies high-quality solutions at a faster speed and significantly reduces the primal integral compared to state-of-the-art solvers and other local search baselines. © 2025, CC BY.

关键词： optimization algorithms

Nesterov’s method of dichotomy via Order Oracle: The problem of optimizing a two-variable function on a square

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arXiv 2024年

作者： Chervonenkis, Boris Krasnov, Andrei Gasnikov, Alexander Lobanov, Aleksandr Moscow Institute of Physics and Technology Russia Institute for Information Transmission Problems RAS Moscow Russia Innopolis University Russia Skolkovo Institute of Science and Technology Russia The Institute for System Programming The Russian Academy of Sciences Russia

The challenges of black box optimization arise due to imprecise responses and limited output information. This article describes new results on optimizing multivariable functions using an Order Oracle, which provides access only to the order between function values and with some small errors. We obtained convergence rate estimates for the one-dimensional search method (golden ratio method) under the condition of oracle inaccuracy, as well as convergence results for the algorithm on a "square" (also with noise), which outperforms its alternatives. The results obtained are similar to those in problems with oracles providing significantly more information about the optimized function. Additionally, the practical application of the algorithm has been demonstrated in maximizing a preference function, where the parameters are the acidity and sweetness of the drink. This function is expected to be convex or at least quasi-convex. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

Overcoming Brittleness in Pareto-Optimal Learning-Augmented algorithms

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arXiv 2024年

作者： Angelopoulos, Spyros Dürr, Christoph Elenter, Alex Lefki, Yanni Sorbonne Université CNRS LIP6 Paris France Ecole Polytechnique Palaiseau France

The study of online algorithms with machine-learned predictions has gained considerable prominence in recent years. One of the common objectives in the design and analysis of such algorithms is to attain (Pareto) optimal tradeoffs between the consistency of the algorithm, i.e., its performance assuming perfect predictions, and its robustness, i.e., the performance of the algorithm under adversarial predictions. In this work, we demonstrate that this optimization criterion can be extremely brittle, in that the performance of Pareto-optimal algorithms may degrade dramatically even in the presence of imperceptive prediction error. To remedy this drawback, we propose a new framework in which the smoothness in the performance of the algorithm is enforced by means of a user-specified profile. This allows us to regulate the performance of the algorithm as a function of the prediction error, while simultaneously maintaining the analytical notion of consistency/robustness tradeoffs, adapted to the profile setting. We apply this new approach to a well-studied online problem, namely the one-way trading problem. For this problem, we further address another limitation of the state-of-the-art Pareto-optimal algorithms, namely the fact that they are tailored to worst-case, and extremely pessimistic inputs. We propose a new Pareto-optimal algorithm that leverages any deviation from the worst-case input to its benefit, and introduce a new metric that allows us to compare any two Pareto-optimal algorithms via a dominance relation. © 2024, CC BY-NC-SA.

关键词： optimization algorithms

A Framework for Decentralized Equilibrium Seeking in Networked Sampled-Data Games using Hybrid Control Tools*

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IFAC-PapersOnLine 2023年第2期56卷 5095-5100页

作者： Vishal Shenoy Jorge I. Poveda Department of Electrical and Computer Engineering University of California San Diego CA 92093 USA

We study the steady-state Nash equilibrium-seeking problem for sampled-data games with LTI dynamics and quadratic costs. The key challenge is to guarantee the robust stability and convergence properties of the closed-loop system in the presence of local individual sampling mechanisms assigned to each of the players in the game. This problem is non-trivial due to the unstable behaviors that can arise when sequential control updates (rather than parallel) emerge in the closed-loop system because of the existence of local control triggering mechanisms in each node of the network. To address this issue, we introduce a controls framework based on tools from hybrid dynamical systems theory. Our results are illustrated via numerical examples.

关键词： Equilibrium-Seeking Network Systems Hybrid Control optimization algorithms Cyber-Physical Systems