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(PDF) A comparison of Bayesian/sampling global optimization techniques
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{"work":{"id":28226947,"created_at":"2016-09-03T09:56:33.003-07:00","from_world_paper_id":157157259,"updated_at":"2024-11-14T14:37:39.859-08:00","_data":{"grobid_abstract":"A survey of current global optimization techniques for continuous variables is presented, inspired by recent publications of computer coding of several popular Bayesiadsampling methods. The methods of Perttunen, Stuckman, Mockus, Zilinskas, and Shaltenis are compared with other global optimization algorithms, specifically, a clustering algorithm, a simulated annealing algorithm and the Monte Carlo method. Results are given for these methods based upon the experimental rate of convergence on a series of standard test functions. A new test function is presented that has a global solution within an area that is small in comparison with the search space. Bruce E. Stuckman (S'X\u0026M'81-SM'90) received the B.","publication_date":"1992,,","publication_name":"IEEE Transactions on Systems, Man, and Cybernetics","grobid_abstract_attachment_id":"48542468"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"A comparison of Bayesian/sampling global optimization techniques","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [52901608]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":48542468,"attachmentType":"pdf"}"><img alt="First page of “A comparison of Bayesian/sampling global optimization techniques”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/48542468/mini_magick20190203-3426-1k87r7o.png?1549211657" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">A comparison of Bayesian/sampling global optimization techniques</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="52901608" href="https://independent.academia.edu/BruceStuckman"><img alt="Profile image of Bruce Stuckman" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Bruce Stuckman</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">1992, IEEE Transactions on Systems, Man, and Cybernetics</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">9 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 28226947; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">A survey of current global optimization techniques for continuous variables is presented, inspired by recent publications of computer coding of several popular Bayesiadsampling methods. The methods of Perttunen, Stuckman, Mockus, Zilinskas, and Shaltenis are compared with other global optimization algorithms, specifically, a clustering algorithm, a simulated annealing algorithm and the Monte Carlo method. Results are given for these methods based upon the experimental rate of convergence on a series of standard test functions. A new test function is presented that has a global solution within an area that is small in comparison with the search space. Bruce E. Stuckman (S'X&M'81-SM'90) received the B.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":48542468,"attachmentType":"pdf","workUrl":"https://www.academia.edu/28226947/A_comparison_of_Bayesian_sampling_global_optimization_techniques"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":48542468,"attachmentType":"pdf","workUrl":"https://www.academia.edu/28226947/A_comparison_of_Bayesian_sampling_global_optimization_techniques"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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The algorithms proposed here combine di!erent methods found in the literature and they are compared with well-established approaches in the corresponding areas. Computational results are obtained on 77 small to moderate size (up to 10 variables) nonlinear test functions with simple bounds and 18 large size test functions (up to 400 variables) collected from literature. Scope and purpose Global optimization techniques aim at identifying the global optimum solution of a function which need not be convex or di!erentiable. In this paper, we consider stochastic global optimization search techniques. Among them, one can name probabilistic search methods such as simulated annealing, genetic algorithms, controlled random search and clustering methods. Another class of global optimization methods considered here are adaptive partitioning algorithms which aim at reducing the search space around the global optimum by consecutive partitioning iterations of a promising subregion into smaller and smaller subspaces. Here, we speci"cally investigate simulated annealing, clustering methods and adaptive partitioning algorithms. Besides implementing some well-established techniques in these "elds, we develop new techniques which also lead to hybrid methods combined with existing ones. In an adaptive partitioning algorithm proposed here a new partition evaluation measure of fuzzy nature is developed. Furthermore, both simulated annealing and random search are used for collecting samples in the adaptive partitioning algorithm. We conduct a study on</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Experiments with new stochastic global optimization search techniques","attachmentId":45595698,"attachmentType":"pdf","work_url":"https://www.academia.edu/25295617/Experiments_with_new_stochastic_global_optimization_search_techniques","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/25295617/Experiments_with_new_stochastic_global_optimization_search_techniques"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="24986776" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/24986776/The_GLOBAL_optimization_method_revisited">The GLOBAL optimization method revisited</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="48167739" href="https://independent.academia.edu/JulioBanga">Julio Banga</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32451810" href="https://independent.academia.edu/L%C3%A1szl%C3%B3P%C3%A1l">László Pál</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Optimization Letters, 2008</p><p class="ds-related-work--abstract ds2-5-body-sm">The multistart clustering global optimization method called GLOBAL has been introduced in the 1980s for bound constrained global optimization problems with black-box type objective function. Since then the technological environment has been changed much. The present paper describes shortly the revisions and updates made on the involved algorithms to utilize the novel technologies, and to improve its reliability. We discuss in detail the results of the numerical comparison with the old version and with C-GRASP, a continuous version of the GRASP method. According to these findings, the new version of GLOBAL is both more reliable and more efficient than the old one, and it compares favorably with C-GRASP too.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"The GLOBAL optimization method revisited","attachmentId":45314083,"attachmentType":"pdf","work_url":"https://www.academia.edu/24986776/The_GLOBAL_optimization_method_revisited","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/24986776/The_GLOBAL_optimization_method_revisited"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="6679394" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/6679394/A_survey_on_the_global_optimization_problem_General_theory_and_computational_approaches">A survey on the global optimization problem: General theory and computational approaches</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="10875964" href="https://unimib.academia.edu/FrancescoArchetti">Francesco Archetti</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Annals of Operations Research, 1984</p><p class="ds-related-work--abstract ds2-5-body-sm">Several different approaches have been suggested for the numerical solution of the global optimization problem: space covering methods, trajectory methods, random sampling, random search and methods based on a stochastic model of the objective function are considered in this paper and their relative computational effectiveness is discussed. A closer analysis is performed of random sampling methods along with cluster analysis of sampled data and of Bayesian nonparametric stopping rules.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A survey on the global optimization problem: General theory and computational approaches","attachmentId":48757452,"attachmentType":"pdf","work_url":"https://www.academia.edu/6679394/A_survey_on_the_global_optimization_problem_General_theory_and_computational_approaches","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/6679394/A_survey_on_the_global_optimization_problem_General_theory_and_computational_approaches"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="64327232" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/64327232/Comparative_Analysis_of_Continuous_Global_Optimization_Methods">Comparative Analysis of Continuous Global Optimization Methods</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="41569720" href="https://independent.academia.edu/BratislavPetrovi">Bratislav Petrovi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2013</p><p class="ds-related-work--abstract ds2-5-body-sm">In this paper we evaluate the performance and compare 19 different heuristics for solving continuous global optimization. They are all based on the following metaheuristics: Simulated annealing, Variable neighborhood search, Particle swarm optimization, and Differential evolution. Codes of methods are taken from their authors. The comparison on usual test instances (convex and non-convex) is performed on the same computer. Dimensions of test functions are changed from 10 to 100, thus effectively covering small and large scale problems. The results measured by computational efforts and ranked statistics show that the recent DE-VNS heuristic outperforms the other 18 algorithms on selected problems. Its better performances are noted in solving non-convex problems.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Comparative Analysis of Continuous Global Optimization Methods","attachmentId":76413158,"attachmentType":"pdf","work_url":"https://www.academia.edu/64327232/Comparative_Analysis_of_Continuous_Global_Optimization_Methods","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/64327232/Comparative_Analysis_of_Continuous_Global_Optimization_Methods"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="58857951" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/58857951/Global_optimization_of_expensive_to_evaluate_functions_an_empirical_comparison_of_two_sampling_criteria">Global optimization of expensive-to-evaluate functions: an empirical comparison of two sampling criteria</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="37936668" href="https://independent.academia.edu/EricWalter2">Eric Walter</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Global Optimization, 2009</p><p class="ds-related-work--abstract ds2-5-body-sm">In many global optimization problems motivated by engineering applications, the number of function evaluations is severely limited by time or cost. To ensure that each of these evaluations usefully contributes to the localization of good candidates for the role of global minimizer, a stochastic model of the function can be built to conduct a sequential choice of evaluation points. Based on Gaussian processes and Kriging, the authors have recently introduced the informational approach to global optimization (IAGO) which provides a onestep optimal choice of evaluation points in terms of reduction of uncertainty on the location of the minimizers. To do so, the probability density of the minimizers is approximated using conditional simulations of the Gaussian process model behind Kriging. In this paper, an empirical comparison between the underlying sampling criterion called conditional minimizer entropy (CME) and the standard expected improvement sampling criterion (EI) is presented. Classical tests functions are used as well as sample paths of the Gaussian model and an actual industrial application. They show the interest of the CME sampling criterion in terms of evaluation savings.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Global optimization of expensive-to-evaluate functions: an empirical comparison of two sampling criteria","attachmentId":73066146,"attachmentType":"pdf","work_url":"https://www.academia.edu/58857951/Global_optimization_of_expensive_to_evaluate_functions_an_empirical_comparison_of_two_sampling_criteria","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/58857951/Global_optimization_of_expensive_to_evaluate_functions_an_empirical_comparison_of_two_sampling_criteria"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="48503991" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/48503991/Global_optimization_of_statistical_functions_with_simulated_annealing">Global optimization of statistical functions with simulated annealing</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="119661130" href="https://independent.academia.edu/GaryFerrier">Gary Ferrier</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Econometrics, 1994</p><p class="ds-related-work--abstract ds2-5-body-sm">Many statistical methods rely on numerical optimization to estimate a model's parameters. Unfortunately, conventional algorithms sometimes fail. Even when they do converge, there is no assurance that they have found the global, rather than a local, optimum. We test a new optimization algorithm, simulated annealing, on four econometric problems and compare it to three common conventional algorithms. Not only can simulated annealing find the global optimum, it is also less likely to fail on difficult functions because it is a very robust algorithm. The promise of simulated annealing is demonstrated on the four econometric problems.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Global optimization of statistical functions with simulated annealing","attachmentId":67081498,"attachmentType":"pdf","work_url":"https://www.academia.edu/48503991/Global_optimization_of_statistical_functions_with_simulated_annealing","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/48503991/Global_optimization_of_statistical_functions_with_simulated_annealing"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="124519258" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/124519258/A_Bi_Objective_Optimization_Based_Acquisition_Strategy_for_Batch_Bayesian_Global_Optimization">A Bi-Objective Optimization Based Acquisition Strategy for Batch Bayesian Global Optimization</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="114464028" href="https://independent.academia.edu/SimoneMagistri">Simone Magistri</a></div><p class="ds-related-work--metadata ds2-5-body-xs">arXiv (Cornell University), 2024</p><p class="ds-related-work--abstract ds2-5-body-sm">In this paper, we deal with batch Bayesian Optimization (Bayes-Opt) problems over a box and we propose a novel bi-objective optimization (BOO) acquisition strategy to sample points where to evaluate the objective function. The BOO problem involves the Gaussian Process posterior mean and variance functions, which, in most of the acquisition strategies from the literature, are generally used in combination, frequently through scalarization. However, such scalarization could compromise the Bayes-Opt process performance, as getting the desired trade-off between exploration and exploitation is not trivial in most cases. We instead aim to reconstruct the Pareto front of the BOO problem based on optimizing both the posterior mean as well as the variance, thus generating multiple tradeoffs without any a priori knowledge. The reconstruction is performed through the Non-dominated Sorting Memetic Algorithm (NSMA), recently proposed in the literature and proved to be effective in solving hard MOO problems. Finally, we present two clustering approaches, each of them operating on a different space, to select potentially optimal points from the Pareto front. We compare our methodology with well-known acquisition strategies from the literature, showing its effectiveness on a wide set of experiments.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Bi-Objective Optimization Based Acquisition Strategy for Batch Bayesian Global Optimization","attachmentId":118728533,"attachmentType":"pdf","work_url":"https://www.academia.edu/124519258/A_Bi_Objective_Optimization_Based_Acquisition_Strategy_for_Batch_Bayesian_Global_Optimization","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/124519258/A_Bi_Objective_Optimization_Based_Acquisition_Strategy_for_Batch_Bayesian_Global_Optimization"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="29344409" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/29344409/Data_Sampling_Using_Bayesian_Analysis_and_its_Applications_in_Simulated_Annealing">Data Sampling Using Bayesian Analysis and its Applications in Simulated Annealing</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="103641" href="https://illinois.academia.edu/BenjaminWah">Benjamin Wah</a></div><p class="ds-related-work--abstract ds2-5-body-sm">In this paper, we propose a new probabilistic sampling procedure and its application in simulated annealing (SA). The new procedure uses Bayesian analysis to evaluate samples made already and draws the next sample based on a density function constructed through Bayesian analysis. After integrating our procedure in SA, we apply it to solve a set of optimization benchmarks. Our results show that our proposed procedure, when used in SA, is very effective in generating highquality samples that are more reliable and robust in leading to global solutions.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Data Sampling Using Bayesian Analysis and its Applications in Simulated Annealing","attachmentId":49786503,"attachmentType":"pdf","work_url":"https://www.academia.edu/29344409/Data_Sampling_Using_Bayesian_Analysis_and_its_Applications_in_Simulated_Annealing","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/29344409/Data_Sampling_Using_Bayesian_Analysis_and_its_Applications_in_Simulated_Annealing"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="56068697" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/56068697/A_hybrid_approach_for_global_optimization">A hybrid approach for global optimization</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="170438895" href="https://independent.academia.edu/RicardoLuizUtschdeFreitasPinto">Ricardo Luiz Utsch de Freitas Pinto</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the Third Metaheuristics International …</p><p class="ds-related-work--abstract ds2-5-body-sm">Mathematical programming methods have been used to solve optimization problems. However, in the presence of local optima, these methods usually fail due to the nature of its search process. On the other hand, genetic algorithms adopt a probabilistic treatment for the variables that qualify them for the solution of global optimization problems. This work develops a hybrid genetic algorithm adapted to optimize multimodal continuous functions that combines the characteristics of global search and versatility of genetic algorithms with the e ciency and precision of local search of mathematical programming algorithms. The results reveal that the proposed hybrid genetic approach is e cient i n determination of the global optimum.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A hybrid approach for global optimization","attachmentId":71636443,"attachmentType":"pdf","work_url":"https://www.academia.edu/56068697/A_hybrid_approach_for_global_optimization","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/56068697/A_hybrid_approach_for_global_optimization"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="28282347" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/28282347/A_global_search_method_for_optimizing_nonlinear_systems">A global search method for optimizing nonlinear systems</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="52901608" href="https://independent.academia.edu/BruceStuckman">Bruce Stuckman</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Transactions on Systems, Man, and Cybernetics, 1988</p><p class="ds-related-work--abstract ds2-5-body-sm">Aktruct -The theon and implementation of a new global search method of optimiration in n dinlensions ir presented, inspired by Kushner'5 method in one dimension. This method is meant to address optimization problem\ where the function has many extrema, where it may or may not be differentiable, and where it is important to reduce the nuniber of ebaluations of the function at the expense of increased computation. Compariwnr are made to the performance of other global optimi7ation technique\ on a set of \tandard differentiable test functions. A new cla\s of diwrete-valued test functions is introduced, and the performance of the method is determined on a randomly generated set of these functions. Overall, this method containr the power of other Bayesian/sanipling techniques without the need of a separate local optimization technique for improied comergence. This yields the ability for the search to operate on utik~iowti functions which ma) contain one or more discrete componentr.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A global search method for optimizing nonlinear systems","attachmentId":48612040,"attachmentType":"pdf","work_url":"https://www.academia.edu/28282347/A_global_search_method_for_optimizing_nonlinear_systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/28282347/A_global_search_method_for_optimizing_nonlinear_systems"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":48542468,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":48542468,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_48542468" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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