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{"work":{"id":88315522,"created_at":"2022-10-11T22:39:31.619-07:00","from_world_paper_id":217234816,"updated_at":"2024-11-22T23:42:05.682-08:00","_data":{"grobid_abstract":"With the advent of efficient techniques for multi-objective evolutionary optimization (EMO), real-world search and optimization problems are being increasingly solved for multiple conflicting objectives. During the past decade, most emphasis has been spent on finding the complete Pareto-optimal set, although EMO researchers were always aware of the importance of procedures which would help choose one particular solution from the Pareto-optimal set for implementation. This is also one of the main issues on which the classical and EMO philosophies are divided on. In this paper, we address this long-standing issue and suggest an interactive EMO procedure which, for the first time, will involve a decision-maker in the evolutionary optimization process and help choose a single solution at the end. This study is the culmination of many year's of research on EMO and would hopefully encourage both practitioners and researchers to pay more attention in viewing the multi-objective optimization as an aggregate task of optimization and decision-making.","publication_date":"2005,,","publication_name":"Lecture Notes in Computer Science","grobid_abstract_attachment_id":"92312665"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"I-EMO: An Interactive Evolutionary Multi-objective Optimization Tool","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [239942384]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; 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":92312665,"attachmentType":"pdf"}"><img alt="First page of “I-EMO: An Interactive Evolutionary Multi-objective Optimization Tool”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/92312665/mini_magick20221012-1-4qibe7.png?1665556871" /><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">I-EMO: An Interactive Evolutionary Multi-objective Optimization Tool</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="239942384" href="https://independent.academia.edu/ShamikChaudhuri"><img alt="Profile image of Shamik Chaudhuri" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/239942384/95143268/84129812/s65_shamik.chaudhuri.jpeg" />Shamik Chaudhuri</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2005, 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">6 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 = 88315522; 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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">With the advent of efficient techniques for multi-objective evolutionary optimization (EMO), real-world search and optimization problems are being increasingly solved for multiple conflicting objectives. During the past decade, most emphasis has been spent on finding the complete Pareto-optimal set, although EMO researchers were always aware of the importance of procedures which would help choose one particular solution from the Pareto-optimal set for implementation. This is also one of the main issues on which the classical and EMO philosophies are divided on. In this paper, we address this long-standing issue and suggest an interactive EMO procedure which, for the first time, will involve a decision-maker in the evolutionary optimization process and help choose a single solution at the end. This study is the culmination of many year's of research on EMO and would hopefully encourage both practitioners and researchers to pay more attention in viewing the multi-objective optimization as an aggregate task of optimization and decision-making.</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":92312665,"attachmentType":"pdf","workUrl":"https://www.academia.edu/88315522/I_EMO_An_Interactive_Evolutionary_Multi_objective_Optimization_Tool"}">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":92312665,"attachmentType":"pdf","workUrl":"https://www.academia.edu/88315522/I_EMO_An_Interactive_Evolutionary_Multi_objective_Optimization_Tool"}"><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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Often, it is very dif®cult to weight the criteria exactly before alternatives are known. Multi-Objective Evolutionary Algorithms based on the principle of Pareto optimality are designed to explore the complete set of non-dominated solutions, which then allows the user to choose among many alternatives. However, although it is very dif®cult to exactly de®ne the weighting of different optimization criteria, usually the user has some notion as to what range of weightings might be reasonable. In this paper, we present a novel, simple, and intuitive way to integrate the user's preference into the evolutionary algorithm by allowing to de®ne linear maximum and minimum trade-off functions. On a number of test problems we show that the proposed algorithm ef®ciently guides the population towards the interesting region, allowing a faster convergence and a better coverage of this area of the Pareto optimal front. q (J. Branke). 1 A solution is called Pareto optimal if, when compared to any other solution, it is superior in at least one criterion.</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":"Guidance in evolutionary multi-objective optimization","attachmentId":47431647,"attachmentType":"pdf","work_url":"https://www.academia.edu/10337282/Guidance_in_evolutionary_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/10337282/Guidance_in_evolutionary_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="1" data-entity-id="88315525" 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/88315525/I_MODE_An_Interactive_Multi_objective_Optimization_and_Decision_Making_Using_Evolutionary_Methods">I-MODE: An Interactive Multi-objective Optimization and Decision-Making Using Evolutionary 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="239942384" href="https://independent.academia.edu/ShamikChaudhuri">Shamik Chaudhuri</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Lecture Notes in Computer Science</p><p class="ds-related-work--abstract ds2-5-body-sm">With the popularity of efficient multi-objective evolutionary optimization (EMO) techniques and the need for such problem-solving activities in practice, EMO methodologies and EMO research and application have received a great deal of attention in the recent past. The first decade of research in EMO area has been spent on developing efficient algorithms for finding a well-converged and well-distributed set of Pareto-optimal solutions, although EMO researchers were always aware of the importance of procedures which would help choose one particular solution from the Pareto-optimal set for implementation. In this paper, we address this long-standing issue and suggest an interactive EMO procedure by collating most salient research in EMO and putting together a step-by-step EMO and decision-making procedure. The idea is implemented in a GUI-based, user-friendly software which allows a user to supply the problem mathematically or by using user-defined macros and enables the user to evaluate solutions directly or by calling an executable software, such as popularly-used MATLAB software for a local search or ANSYS software for finite element analysis, etc. Starting with standard EMO applications, continuing to finding robust, partial, and user-defined preferred frontiers through standard MCDM procedures, the well-coordinated software allows the user to first have an idea of the complete trade-off frontier, then systematically focus in preferred regions, and finally choose a single solution for implementation.</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":"I-MODE: An Interactive Multi-objective Optimization and Decision-Making Using Evolutionary Methods","attachmentId":92312669,"attachmentType":"pdf","work_url":"https://www.academia.edu/88315525/I_MODE_An_Interactive_Multi_objective_Optimization_and_Decision_Making_Using_Evolutionary_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/88315525/I_MODE_An_Interactive_Multi_objective_Optimization_and_Decision_Making_Using_Evolutionary_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="2" data-entity-id="94148517" 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/94148517/ParadisEO_MOEO_A_Framework_for_Evolutionary_Multi_objective_Optimization">ParadisEO-MOEO: A Framework for Evolutionary 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="9335826" href="https://independent.academia.edu/LJourdan">Laetitia Jourdan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Lecture Notes in Computer Science</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper presents ParadisEO-MOEO, a white-box objectoriented generic framework dedicated to the flexible design of evolutionary multi-objective algorithms. This paradigm-free software embeds some features and techniques for Pareto-based resolution and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. It is based on a clear conceptual distinction between the solution methods and the multi-objective problems they are intended to solve. This separation confers a maximum design and code reuse. ParadisEO-MOEO provides a broad range of archive-related features (such as elitism or performance metrics) and the most common Pareto-based fitness assignment strategies (MOGA, NSGA, SPEA, IBEA and more). Furthermore, parallel and distributed models as well as hybridization mechanisms can be applied to an algorithm designed within ParadisEO-MOEO using the whole version of ParadisEO. In addition, GUIMOO, a platform-independant free software dedicated to results analysis for multi-objective problems, is briefly introduced.</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":"ParadisEO-MOEO: A Framework for Evolutionary Multi-objective Optimization","attachmentId":96686829,"attachmentType":"pdf","work_url":"https://www.academia.edu/94148517/ParadisEO_MOEO_A_Framework_for_Evolutionary_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/94148517/ParadisEO_MOEO_A_Framework_for_Evolutionary_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="3" data-entity-id="83330409" 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/83330409/A_review_of_multi_objective_optimisation_and_decision_making_using_evolutionary_algorithms">A review of multi-objective optimisation and decision making using evolutionary algorithms</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="702751" href="https://bml.academia.edu/MuneendraOjha">Muneendra Ojha</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Bio-Inspired Computation, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Research in the field of multi-objective optimisation problem (MOP) has garnered ample interest in the last two decades. Majority of methods developed for solving the problem belong to the class of evolutionary algorithms (EA) which are population-based evolution search strategies involving exploration and exploitation in general. Multi-criteria decision making (MCDM) is another aspect of MOP which involves finding methods to help a decision maker (DM) in making most optimal decisions in a conflicting scenario. In this paper, we present a brief review of the methods and techniques developed in the last 15 years which try to solve the MOP and MCDM problems. The strengths and weaknesses of methods have been discussed to present a holistic view. This paper covers challenges associated with MOEAs, different solution approaches such as Pareto-based methods and non-Pareto methods, indicator-based methods, aggregation methods, decomposition-based methods, methods using reference sets, MOEAs involving DM, a priori, interactive and a posteriori preference incorporation methods. It also discusses most of the quality metrics and performance indicators proposed in the literature along with benchmark problems. In addition, some future research issues and directions are also 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":"A review of multi-objective optimisation and decision making using evolutionary algorithms","attachmentId":88711188,"attachmentType":"pdf","work_url":"https://www.academia.edu/83330409/A_review_of_multi_objective_optimisation_and_decision_making_using_evolutionary_algorithms","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/83330409/A_review_of_multi_objective_optimisation_and_decision_making_using_evolutionary_algorithms"><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="2863932" 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/2863932/The_good_of_the_many_outweighs_the_good_of_the_one_evolutionary_multi_objective_optimization">The good of the many outweighs the good of the one: evolutionary 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="292046" href="https://manchester.academia.edu/JoshuaKnowles">Joshua Knowles</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2003</p><p class="ds-related-work--abstract ds2-5-body-sm">Abstract. We dwell in largely non-technical terms on the essential differences between single-objective optimization and multiple-objective optimization. We argue in particular that single-objective approaches to real-world problems are almost invariably simplifications of the real-problem which make many ideal solutions unreachable to the optimization method. We promote the use of multi-objective optimization methods, particularly those arising from the evolutionary computation community.</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 good of the many outweighs the good of the one: evolutionary multi-objective optimization","attachmentId":30802716,"attachmentType":"pdf","work_url":"https://www.academia.edu/2863932/The_good_of_the_many_outweighs_the_good_of_the_one_evolutionary_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/2863932/The_good_of_the_many_outweighs_the_good_of_the_one_evolutionary_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="5" data-entity-id="94148519" 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/94148519/A_unified_model_for_evolutionary_multi_objective_optimization_and_its_implementation_in_a_general_purpose_software_framework">A unified model for evolutionary multi-objective optimization and its implementation in a general purpose software framework</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="9335826" href="https://independent.academia.edu/LJourdan">Laetitia Jourdan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making, 2009</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper gives a concise overview of evolutionary algorithms for multiobjective optimization. A substantial number of evolutionary computation methods for multiobjective problem solving has been proposed so far, and an attempt of unifying existing approaches is here presented. Based on a fine-grained decomposition and following the main issues of fitness assignment, diversity preservation and elitism, a conceptual global model is proposed and is validated by regarding a number of state-of-the-art algorithms as simple variants of the same structure. The presented model is then incorporated into a generalpurpose software framework dedicated to the design and the implementation of evolutionary multiobjective optimization techniques: ParadisEO-MOEO. This package has proven its validity and flexibility by enabling the resolution of many real-world and hard multiobjective optimization 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":"A unified model for evolutionary multi-objective optimization and its implementation in a general purpose software framework","attachmentId":96686816,"attachmentType":"pdf","work_url":"https://www.academia.edu/94148519/A_unified_model_for_evolutionary_multi_objective_optimization_and_its_implementation_in_a_general_purpose_software_framework","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/94148519/A_unified_model_for_evolutionary_multi_objective_optimization_and_its_implementation_in_a_general_purpose_software_framework"><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="1332380" 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/1332380/In_Search_of_Proper_Pareto_optimal_Solutions_Using_Multi_objective_Evolutionary_Algorithms">In Search of Proper Pareto-optimal Solutions Using Multi-objective Evolutionary Algorithms</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="1044211" href="https://kit.academia.edu/PradyumnKumarShukla">Pradyumn Kumar Shukla</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computational ScienceICCS 2007, 2007</p><p class="ds-related-work--abstract ds2-5-body-sm">There are multiple solution concepts in multi-objective optimization among which a decision maker has to select some good solutions usually which satisfy some trade-off criteria's. The need for potentially good solutions has always been one of the primary aims in multiobjective optimization. A complete representation of all these solutions is only possible with population based approaches like multi-objective evolutionary algorithms since then trade-off's can be calculated at each generation from the population members. Thus this paper proposes the use of multi-objective evolutionary algorithms for obtaining a complete representation of these good solutions. Theoretical results show how one can integrate search procedure for obtaining these solutions in population based evolutionary algorithms and some convergence results. Finally simulation results are presented on a number of test 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":"In Search of Proper Pareto-optimal Solutions Using Multi-objective Evolutionary Algorithms","attachmentId":8256593,"attachmentType":"pdf","work_url":"https://www.academia.edu/1332380/In_Search_of_Proper_Pareto_optimal_Solutions_Using_Multi_objective_Evolutionary_Algorithms","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/1332380/In_Search_of_Proper_Pareto_optimal_Solutions_Using_Multi_objective_Evolutionary_Algorithms"><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="94148502" 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/94148502/ParadisEO_MOEO_A_Software_Framework_for_Evolutionary_Multi_Objective_Optimization">ParadisEO-MOEO: A Software Framework for Evolutionary 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="9335826" href="https://independent.academia.edu/LJourdan">Laetitia Jourdan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Studies in Computational Intelligence, 2010</p><p class="ds-related-work--abstract ds2-5-body-sm">This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization. This paradigm-free software proposes a unified view for major evolutionary multi-objective metaheuristics. It embeds some features and techniques for multi-objective resolution and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. It is based on a clear conceptual distinction between the solution methods and the problems they are intended to solve. This separation confers a maximum design and code reuse. This general-purpose framework provides a broad range of fitness assignment strategies, the most common diversity preservation mechanisms, some elitistrelated features as well as statistical tools. Furthermore, a number of state-of-the-art search methods, including NSGA-II, SPEA2 and IBEA, have been implemented in a user-friendly way, based on the fine-grained ParadisEO-MOEO components.</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":"ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization","attachmentId":96686801,"attachmentType":"pdf","work_url":"https://www.academia.edu/94148502/ParadisEO_MOEO_A_Software_Framework_for_Evolutionary_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/94148502/ParadisEO_MOEO_A_Software_Framework_for_Evolutionary_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="3189264" 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/3189264/Towards_a_deeper_understanding_of_trade_offs_using_multi_objective_evolutionary_algorithms">Towards a deeper understanding of trade-offs using multi-objective evolutionary algorithms</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="1044211" href="https://kit.academia.edu/PradyumnKumarShukla">Pradyumn Kumar Shukla</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Applications of Evolutionary …, 2012</p><p class="ds-related-work--abstract ds2-5-body-sm">A multi-objective optimization problem is characterized by multiple and conflicting objective functions. The conflicting nature of the objectives gives rise to the notion of trade-offs. A trade-off represents the ratio of change in the objective function values, when one of the objective function values increases and the value of some other objective function decreases. Various notions of trade-offs have been present in the classical multiple criteria decision making community and many scalarization approaches have been proposed in the literature to find a solution satisfying some given trade-off requirements. Almost all of these approaches are point-by-point algorithms. On the other hand, multi-objective evolutionary algorithms work with a population and, if properly designed, are able to find the complete preferred subset of the Pareto-optimal set satisfying an a priori given bound on trade-offs. In this paper, we analyze and put together various notions of trade-offs that we find in the classical literature, classifying them into two groups. We then go on to propose multi-objective evolutionary algorithms to find solutions belonging to the two classified groups. This is done by modifying a state-of-theart evolutionary algorithm NSGA-II. An extensive computational study substantiates the claims of the paper.</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":"Towards a deeper understanding of trade-offs using multi-objective evolutionary algorithms","attachmentId":50420304,"attachmentType":"pdf","work_url":"https://www.academia.edu/3189264/Towards_a_deeper_understanding_of_trade_offs_using_multi_objective_evolutionary_algorithms","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/3189264/Towards_a_deeper_understanding_of_trade_offs_using_multi_objective_evolutionary_algorithms"><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="99671680" 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/99671680/A_Meta_Objective_Approach_for_Many_Objective_Evolutionary_Optimization">A Meta-Objective Approach for Many-Objective Evolutionary 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="32956335" href="https://independent.academia.edu/GaryYen1">Gary Yen</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Evolutionary Computation, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">Pareto-based multi-objective evolutionary algorithms experience grand challenges in solving many-objective optimization problems due to their inability to maintain both convergence and diversity in a high-dimensional objective space. Exiting approaches usually modify the selection criteria to overcome this issue. Different from them, we propose a novel meta-objective (MeO) approach that transforms the many-objective optimization problems in which the new optimization problems become easier to solve by the Pareto-based algorithms. MeO converts a given many-objective optimization problem into a new one, which has the same Pareto optimal solutions and the number of objectives with the original one. Each meta-objective in the new problem consists of two components which measure the convergence and diversity performances of a solution, respectively. Since MeO only converts the problem formulation, it can be readily incorporated within any multi-objective evolutionary algorithms, includin...</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 Meta-Objective Approach for Many-Objective Evolutionary Optimization","attachmentId":100699010,"attachmentType":"pdf","work_url":"https://www.academia.edu/99671680/A_Meta_Objective_Approach_for_Many_Objective_Evolutionary_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/99671680/A_Meta_Objective_Approach_for_Many_Objective_Evolutionary_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></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":92312665,"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":92312665,"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_92312665" 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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class="ds-related-work--metadata ds2-5-body-xs">Engineering Computations, 2002</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":"Pareto‐based continuous evolutionary algorithms for multiobjective optimization","attachmentId":102456221,"attachmentType":"pdf","work_url":"https://www.academia.edu/102102677/Pareto_based_continuous_evolutionary_algorithms_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-related-work-grid-card-view-pdf" href="https://www.academia.edu/102102677/Pareto_based_continuous_evolutionary_algorithms_for_multiobjective_optimization"><span class="ds2-5-text-link__content">View PDF</span><span 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