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{"work":{"id":86409090,"created_at":"2022-09-10T08:52:35.953-07:00","from_world_paper_id":214705573,"updated_at":"2024-11-25T19:26:01.871-08:00","_data":{"grobid_abstract":"This chapter presents a general overview of parallel approaches for multiobjective optimization. For this purpose, we propose a taxonomy for parallel metaheuristics and exact methods. This chapter covers the design aspect of the algorithms as well as the implementation aspects on different parallel and distributed architectures.","publication_date":"2008,,","publication_name":"Lecture Notes in Computer Science","grobid_abstract_attachment_id":"90869435"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Parallel Approaches for Multiobjective Optimization","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true,"seo_quality":null}}["work"]; window.loswp.workCoauthors = [29009872]; 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":90869435,"attachmentType":"pdf"}"><img alt="First page of “Parallel Approaches for Multiobjective Optimization”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/90869435/mini_magick20220910-1-cy31z7.png?1662825311" /><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">Parallel Approaches for Multiobjective Optimization</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="29009872" href="https://independent.academia.edu/G%C3%BCnterRudolph"><img alt="Profile image of Günter Rudolph" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Günter Rudolph</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2008, Lecture Notes in Computer Science</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">24 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 = 86409090; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=86409090"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); 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">This chapter presents a general overview of parallel approaches for multiobjective optimization. For this purpose, we propose a taxonomy for parallel metaheuristics and exact methods. This chapter covers the design aspect of the algorithms as well as the implementation aspects on different parallel and distributed architectures.</p><div class="ds-work-card--button-container"><div class="primary-buttons "><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":90869435,"attachmentType":"pdf","workUrl":"https://www.academia.edu/86409090/Parallel_Approaches_for_Multiobjective_Optimization"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span>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":90869435,"attachmentType":"pdf","workUrl":"https://www.academia.edu/86409090/Parallel_Approaches_for_Multiobjective_Optimization"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></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" data-impression-entity-id="86409090" data-impression-entity-type="2" data-impression-source="signup-banner"><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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In this procedure, a population of solutions is sorted with respect to the values of the objective functions and partitioned into subpopulations which are distributed among the processors. Each processor applies a sequential multiobjective genetic algorithm that we have devised (called Single Front Genetic Algorithm, SFGA) to its subpopulation. Experimental results are provided comparing PSFGA with previously proposed multiobjective evolutionary algorithms.</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":"PSFGA: a parallel genetic algorithm for multiobjective optimization","attachmentId":66617849,"attachmentType":"pdf","work_url":"https://www.academia.edu/47616254/PSFGA_a_parallel_genetic_algorithm_for_multiobjective_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/47616254/PSFGA_a_parallel_genetic_algorithm_for_multiobjective_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="1" data-entity-id="102542140" 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/102542140/Metaheuristics_for_multiobjective_optimisation">Metaheuristics for multiobjective optimisation</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="33255400" href="https://hw.academia.edu/DavidWCorne">David W. Corne</a></div><p class="ds-related-work--metadata ds2-5-body-xs">4OR, 2010</p><p class="ds-related-work--abstract ds2-5-body-sm">This is a summary of the author's PhD thesis supervised by Laetitia Jourdan and El-Ghazali Talbi and defended on 8 December 2009 at the Université Lille 1. The thesis is written in French and is available from http://sites.google.com/ site/arnaudliefooghe/. This work deals with the design, implementation and experimental analysis of metaheuristics for solving multiobjective optimisation problems, with a particular interest on hard and large combinatorial problems from the field of logistics. After focusing on a unified view of multiobjective metaheuristics, we propose new cooperative, adaptive and parallel approaches. The performance of these methods are experimented on a scheduling and a routing problem involving two or three objective functions. We finally discuss how to adapt such metaheuristics during the search process in order to handle uncertainty that may occur from many different sources.</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":"Metaheuristics for multiobjective optimisation","attachmentId":102785858,"attachmentType":"pdf","work_url":"https://www.academia.edu/102542140/Metaheuristics_for_multiobjective_optimisation","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/102542140/Metaheuristics_for_multiobjective_optimisation"><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="61897412" 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/61897412/An_algorithmic_framework_for_multiobjective_optimization">An algorithmic framework for multiobjective 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="5902102" href="https://utp-my.academia.edu/IrraivanElamvazuthi">Irraivan Elamvazuthi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2013</p><p class="ds-related-work--abstract ds2-5-body-sm">Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.</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":"An algorithmic framework for multiobjective optimization","attachmentId":74814674,"attachmentType":"pdf","work_url":"https://www.academia.edu/61897412/An_algorithmic_framework_for_multiobjective_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/61897412/An_algorithmic_framework_for_multiobjective_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="3" data-entity-id="9433714" 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/9433714/PSFGA_Parallel_Processing_and_Evolutionary_Computation_for_Multiobjective_Optimisation">PSFGA: Parallel Processing and Evolutionary Computation for Multiobjective Optimisation</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="21952325" href="https://independent.academia.edu/JulioOrtega7">Julio Ortega</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Parallel Computing, 2004</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper deals with the study of the cooperation between parallel processing and evolutionary computation to obtain efficient procedures for solving multiobjective optimisation problems. We propose a new algorithm called PSFGA (parallel single front genetic algorithm), an elitist evolutionary algorithm for multiobjective problems with a clearing procedure that uses a grid in the objective space for diversity maintaining purposes. Thus, PSFGA is a parallel genetic algorithm with a structured population in the form of a set of islands. The performance analysis of PSFGA has been carried out in a cluster system and experimental results show that our parallel algorithm provides adequate results in both, the quality of the solutions found and the time to obtain them. It has been shown that its sequential version also outperforms other previously proposed sequential procedures for multiobjective optimisation in the cases studied.</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":"PSFGA: Parallel Processing and Evolutionary Computation for Multiobjective Optimisation","attachmentId":47782126,"attachmentType":"pdf","work_url":"https://www.academia.edu/9433714/PSFGA_Parallel_Processing_and_Evolutionary_Computation_for_Multiobjective_Optimisation","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/9433714/PSFGA_Parallel_Processing_and_Evolutionary_Computation_for_Multiobjective_Optimisation"><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="19811714" 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/19811714/PARALLELIZATION_OF_AN_EVOLUTIONARY_ALGORITHM_FOR_MULTIOBJECTIVE_OPTIMIZATION">PARALLELIZATION OF AN EVOLUTIONARY ALGORITHM FOR MULTIOBJECTIVE 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="32507027" href="https://ijs.academia.edu/MatjazDepolli">Matjaz Depolli</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Solving real-life optimization problems numerically is often very time demanding, because of high complexity of the simulations that are usually involved. Solving such problems becomes highly impractical for this reason and can even lead to use of less complex and also less accurate models. Fortunately, evolutionary algorithms, often used in numerical optimization, can be parallelized with relative ease, which significantly reduces the time required for optimization on parallel computer architectures.</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":"PARALLELIZATION OF AN EVOLUTIONARY ALGORITHM FOR MULTIOBJECTIVE OPTIMIZATION","attachmentId":42011397,"attachmentType":"pdf","work_url":"https://www.academia.edu/19811714/PARALLELIZATION_OF_AN_EVOLUTIONARY_ALGORITHM_FOR_MULTIOBJECTIVE_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/19811714/PARALLELIZATION_OF_AN_EVOLUTIONARY_ALGORITHM_FOR_MULTIOBJECTIVE_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="5" data-entity-id="19330944" 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/19330944/Parallel_multi_objective_optimization_using_master_slave_model_on_heterogeneous_resources">Parallel multi-objective optimization using master-slave model on heterogeneous resources</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="39591530" href="https://griffith.academia.edu/AndrewLewis">Andrew Lewis</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2008 IEEE Congress on Evolutionary Computation (CEC), 2008</p><p class="ds-related-work--abstract ds2-5-body-sm">In this paper, we study parallelization of multiobjective optimization algorithms on a set of hetergeneous resources based on the Master-Slave model. Master-Slave model is known to be the simplest parallelization paradigm where a master processor sends the function evaluations to several slave processors. The critical issue when using the standard methods on heterogeneous resources is that in every iteration of the optimization, the master processor has to wait for all of the computing resources (including the slow ones) to deliver the evaluations. In this paper, we study a new algorithm, where all of the available computing resources are efficiently utilized to perform the multi-objective optimization task independent from the speed (fast or slow) of the computing processors. For this we propose a hybrid method using Multi-objective Particle Swarm optimization and Binary search methods. The new algorithm has been tested on a scenario contaning heterogeneous resources and the results show that not only the new algorithm performs well for parallel resources, but also when comparing to a normal serial run on one computer.</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":"Parallel multi-objective optimization using master-slave model on heterogeneous resources","attachmentId":40560693,"attachmentType":"pdf","work_url":"https://www.academia.edu/19330944/Parallel_multi_objective_optimization_using_master_slave_model_on_heterogeneous_resources","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/19330944/Parallel_multi_objective_optimization_using_master_slave_model_on_heterogeneous_resources"><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="68103225" 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/68103225/A_Parallel_Evolutionary_System_for_Multi_objective_Optimisation">A Parallel Evolutionary System for Multi-objective Optimisation</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="29009872" href="https://independent.academia.edu/G%C3%BCnterRudolph">Günter Rudolph</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020 IEEE Congress on Evolutionary Computation (CEC), 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Parallel evolutionary algorithms have been used for solving multiobjective optimization problems. The aim is to find or approximate the Pareto optimal set in a reasonable time. In this work, we present a new approach that divides the objective search-space into different partitions and assigns each processor its corresponding partition. Each processor will try to find the set of solutions for its partition only. The sub-Pareto fronts will be combined later and the parallelisation approach is based on a mutli-start approach by having independent algorithm on every processor with its own starting points. Experimental results on well known test cases showed that the proposed method outperformed several state-of-the-art evolutionary algorithms regarding convergence to the true Pareto front and gave very competitive results when considering the hypervolume metric. Also, superlinear speedup results were achieved for all test functions.</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 Parallel Evolutionary System for Multi-objective Optimisation","attachmentId":78699244,"attachmentType":"pdf","work_url":"https://www.academia.edu/68103225/A_Parallel_Evolutionary_System_for_Multi_objective_Optimisation","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/68103225/A_Parallel_Evolutionary_System_for_Multi_objective_Optimisation"><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="49575845" 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/49575845/Parallel_skeleton_for_multi_objective_optimization">Parallel skeleton for multi-objective 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="58108624" href="https://independent.academia.edu/CarlosSeguraGonz%C3%A1lez">Carlos Segura González</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07, 2007</p><p class="ds-related-work--abstract ds2-5-body-sm">Many real-world problems are based on the optimization of more than one objective function. This work presents a tool for the resolution of multi-objective optimization problems based on the cooperation of a set of algorithms. The invested time in the resolution is decreased by means of a parallel implementation of an evolutionary team algorithm. This model keeps the advantages of heterogeneous island models but also allows to assign more computational resources to the algorithms with better expectations. The elitist scheme applied aims to improve the results obtained with single executions of independent evolutionary algorithms. The user solves the problem without the need of knowing the internal operation details of the used evolutionary algorithms. The computational results obtained on a cluster of PCs for some tests available in the literature are presented.</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":"Parallel skeleton for multi-objective optimization","attachmentId":67897851,"attachmentType":"pdf","work_url":"https://www.academia.edu/49575845/Parallel_skeleton_for_multi_objective_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/49575845/Parallel_skeleton_for_multi_objective_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="8" data-entity-id="117959480" 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/117959480/Designing_parallelism_in_surrogate_assisted_multiobjective_optimization_based_on_decomposition">Designing parallelism in surrogate-assisted multiobjective optimization based on decomposition</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="40951577" href="https://independent.academia.edu/KiyoshiTanaka">Kiyoshi Tanaka</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">On the one hand, surrogate-assisted evolutionary algorithms are established as a method of choice for expensive black-box optimization problems. On the other hand, the growth in computing facilities has seen a massive increase in potential computational power, granted the users accommodate their approaches with the oered parallelism. While a number of studies acknowledge the impact of parallelism for single-objective expensive optimization assisted by surrogates, extending such techniques to the multi-objective setting has not yet been properly investigated, especially within the stateof-the-art decomposition framework. We rst highlight the dierent degrees of parallelism in existing surrogate-assisted multi-objective evolutionary algorithms based on decomposition (S-MOEA/D). We then provide a comprehensive analysis of the key steps towards a successful parallel S-MOEA/D approach. Through an extensive benchmarking eort relying on the well-established bbob-biobj test functions, we analyze the performance of the dierent algorithm designs with respect to the problem dimensionality and difculty, the amount of parallel cores available, and the supervised learning models considered. In particular, we show the dierence in algorithm scalability based on the selected surrogate-assisted approaches, the performance impact of distributing the model training task and the ecacy of the designed parallel-surrogate methods. CCS CONCEPTS • Theory of computation → Parallel algorithms; Gaussian processes; Algorithm design techniques.</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":"Designing parallelism in surrogate-assisted multiobjective optimization based on decomposition","attachmentId":113695184,"attachmentType":"pdf","work_url":"https://www.academia.edu/117959480/Designing_parallelism_in_surrogate_assisted_multiobjective_optimization_based_on_decomposition","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/117959480/Designing_parallelism_in_surrogate_assisted_multiobjective_optimization_based_on_decomposition"><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="79974091" 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/79974091/Techniques_for_highly_multiobjective_optimisation">Techniques for highly multiobjective optimisation</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="14030858" href="https://hw.academia.edu/DavidCorne">David Corne</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07, 2007</p><p class="ds-related-work--abstract ds2-5-body-sm">The research area of evolutionary multiobjective optimization (EMO) is reaching better understandings of the properties and capabilities of EMO algorithms, and accumulating much evidence of their worth in practical scenarios. An urgent emerging issue is that the favoured EMO algorithms scale poorly when problems have 'many' (e.g. five or more) objectives. One of the chief reasons for this is believed to be that, in many-objective EMO search, populations are likely to be largely composed of nondominated solutions. In turn, this means that the commonly-used algorithms cannot distinguish between these for selective purposes. However, there are methods that can be used validly to rank points in a nondominated set, and may therefore usefully underpin selection in EMO search. Here we discuss and compare several such methods. Our main finding is that simple variants of the often-overlooked 'Average Ranking' strategy usually outperform other methods tested, covering problems with 5-20 objectives and differing amounts of inter-objective correlation.</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":"Techniques for highly multiobjective optimisation","attachmentId":86509862,"attachmentType":"pdf","work_url":"https://www.academia.edu/79974091/Techniques_for_highly_multiobjective_optimisation","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/79974091/Techniques_for_highly_multiobjective_optimisation"><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":90869435,"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":90869435,"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_90869435" 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. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="122080882" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/122080882/Parallel_metaheuristics_recent_advances_and_new_trends">Parallel metaheuristics: recent advances and new trends</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32702460" href="https://udelar.academia.edu/SNesmachnow">S. 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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-related-work-sidebar-card" data-collection-position="5" data-entity-id="118661939" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/118661939/Multiobjective_Optimization">Multiobjective Optimization</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="66435901" href="https://independent.academia.edu/Lunamajumder">Luna majumder</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Optimization Techniques and Applications with Examples, 2018</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" 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href="https://www.academia.edu/45549232/Multiobjective_optimization_using_an_adaptive_weighting_scheme">Multiobjective optimization using an adaptive weighting scheme</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="166136236" href="https://independent.academia.edu/ShubhangiDeshpande3">Shubhangi Deshpande</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Optimization Methods and Software, 2015</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":"Multiobjective optimization using an adaptive weighting scheme","attachmentId":66027385,"attachmentType":"pdf","work_url":"https://www.academia.edu/45549232/Multiobjective_optimization_using_an_adaptive_weighting_scheme","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" 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href="https://www.academia.edu/14124826/MULTIOBJECTIVE_OPTIMIZATION_USING_PARALLEL_VECTOR_EVALUATED_PARTICLE_SWARM_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-related-work-sidebar-card" data-collection-position="8" data-entity-id="283901" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/283901/Global_Multiobjective_Optimization_Using_Evolutionary_Algorithms">Global Multiobjective Optimization Using Evolutionary Algorithms</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="227503" href="https://fhnw.academia.edu/HanneThomas">Hanne Thomas</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Heuristics, 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