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Journal articles on the topic 'Objectifs de parité'

Author: Grafiati

Published: 4 April 2023

Last updated: 31 July 2025

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1

Haou, Abir, Kamel Miroud, and Djallel Eddine Gherissi. "Impact des caractéristiques du troupeau et des pratiques d’élevage sur les performances de reproduction des vaches laitières dans le Nord-Est algérien." Revue d’élevage et de médecine vétérinaire des pays tropicaux 74, no. 4 (2021): 183–91. http://dx.doi.org/10.19182/remvt.36798.

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L’étude a porté sur les effets des caractéristiques du troupeau (race, taille, parité et zone d’étude) et des pratiques d’élevage (chaleurs induites/naturelles, pratique du flushing ou non, et durée du tarissement) sur les taux de fécondité et de fertilité de 721 vaches laitières (VL) des races Montbéliarde (n = 379) et Prim’Holstein (n = 342) réparties sur 23 troupeaux, nées et mises à la reproduction en Algérie. Les paramètres de fécondité ont révélé un intervalle entre le vêlage et les premières chaleurs de 86,8 ± 48 jours, entre le vêlage et la première insémination artificielle (IA) de 10

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François Régis, Sindayihebura Jean, Bouba Djourdebbe Franklin, Nganawara Didier, et al. "Qui Sont les Femmes en Union Sans Intention d’Utilisation de la Contraception Moderne au Burundi ? Etude du Profil Socio-Démographique à Partir des Données de 2010 et 2016-2017." European Scientific Journal, ESJ 19, no. 14 (2023): 159. http://dx.doi.org/10.19044/esj.2023.v19n14p159.

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Dans un contexte qui réclame la maîtrise de la fécondité pour atteindre les objectifs nationaux et mondiaux du développement, la prévalence contraceptive au Burundi reste faible. De surcroît, la proportion des femmes en union sans intention de recourir à la contraception moderne a récemment augmenté. Cette proportion est passée de 53% en 2010 à 66% en 2016-2017. En se basant sur les données des Enquêtes Démographiques et de Santé du Burundi (EDSB) réalisées en 2010 et 2016-2017, cette étude a pour objectif de déterminer le profil socio-démographique des femmes sans intention de contraception m

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VOCORET, M., C. CARDINALE, A. AGOSTINI, F. VALLE, R. GIORGI, and H. BENJELLOUM. "L INTERRUPTION VOLONTAIRE DE GROSSESSE PAR VOIE MEDICAMENTEUSE HORS ETABLISSEMENT DE SANTE." EXERCER 35, no. 206 (2024): 347–53. http://dx.doi.org/10.56746/exercer.2024.206.347.

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Introduction. La forte tension hospitalière pendant la pandémie de Covid-19 a amené les pouvoirs publics à augmenter le délai de l’interruption volontaire de grossesse médicamenteuse (IVGM) hors établissement de santé. Objectifs. Mesurer l’efficacité de l’IVGM entre 7 et 9 semaines d’aménorrhée (SA). Comparer les caractéristiques des patientes réalisant une IVGM entre 7 et 9 SA avec celles des patients réalisant une IVGM avant 7 SA. Méthode. Étude observationnelle descriptive transversale monocentrique multisite, au sein des centres de planification et d’éducation familiale des Bouches-du-Rhôn

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Ivensky, Victoria, Romain Mandel, Annie-Claude Boulay, Christian Lavallée, Janie Benoît, and Annie-Claude Labbé. "Dépistage prénatal sous-optimal des infections à Chlamydia trachomatis et Neisseria gonorrhoeae dans un centre des naissances et de soins tertiaires de Montréal : une étude de cohorte rétrospective." Relevé des maladies transmissibles au Canada 47, no. 04 (2021): 228–35. http://dx.doi.org/10.14745/ccdr.v47i04a05f.

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Contexte : La Société canadienne de pédiatrie ne recommande plus la prophylaxie oculaire universelle avec l’onguent d’érythromycine pour prévenir la conjonctivite néonatale. Le dépistage des infections à Chlamydia trachomatis et à Neisseria gonorrhoeae chez toutes les femmes enceintes est considéré comme le moyen le plus efficace de prévenir la transmission verticale et la conjonctivite néonatale. Objectif : Les objectifs de l’étude étaient d’évaluer les taux de dépistage prénatal des infections à C. trachomatis et à N. gonorrhoeae et de comparer les facteurs sociodémographiques entre les pers

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Mpoy, Charles Wembonyama, Barry Mukwarari Katembo, Willy Kakozi Missumba, and Xavier K. Kinenkinda. "Étude de la mort fœtale in utero à Lubumbashi, République Démocratique du Congo." Revue de l’Infirmier Congolais 6, no. 1 (2022): 21–27. http://dx.doi.org/10.62126/zqrx.2022614.

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Introduction. La mort fœtale in utéro (MFIU) constitue un véritable problème en obstétrique avec une fréquence élevée dans les pays en développement. Le présent travail s’est fixé comme objectifs de déterminer la fréquence de mort fœtale in utero à l’hôpital général de référence provincial Jason Sendwe, de décrire le profil épidémio-clinique des accouchées et d’identifier les étiologies probables. Matériel et méthodes. Il s’agit d'une étude descriptive transversale avec mode de récolte prospectif, portant sur 63 accouchements des fœtus mort in utero, des grossesses monofœtale et d’âge gestatio

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Červeňanská, Zuzana, Janette Kotianová, Pavel Važan, Bohuslava Juhásová, and Martin Juhás. "Multi-Objective Optimization of Production Objectives Based on Surrogate Model." Applied Sciences 10, no. 21 (2020): 7870. http://dx.doi.org/10.3390/app10217870.

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The article addresses an approximate solution to the multi-objective optimization problem for a black-box function of a manufacturing system. We employ the surrogate of the discrete-event simulation model of a batch production system in an analytical form. Integration of simulation, Design of Experiments methods, and Weighted Sum and Weighted Product multi-objective methods are used in an arrangement of a priori defined preferences to find a solution near the Pareto optimal solution in a criterion space. We compare the results obtained through the analytical approach to the outcomes of simulat

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Liu, Qi, Jiahao Liu, and Dunhu Liu. "Intelligent Multi-Objective Public Charging Station Location with Sustainable Objectives." Sustainability 10, no. 10 (2018): 3760. http://dx.doi.org/10.3390/su10103760.

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This paper investigates a multi-objective charging station location model with the consideration of the triple bottom line principle for green and sustainable development from economic, environmental and social perspectives. An intelligent multi-objective optimization approach is developed to handle this problem by integrating an improved multi-objective particle swarm optimization (MOPSO) process and an entropy weight method-based evaluation process. The MOPSO process is utilized to obtain a set of Pareto optimal solutions, and the entropy weight method-based evaluation process is utilized to

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Li, Yifan, Hai-Lin Liu, and E. D. Goodman. "Hyperplane-Approximation-Based Method for Many-Objective Optimization Problems with Redundant Objectives." Evolutionary Computation 27, no. 2 (2019): 313–44. http://dx.doi.org/10.1162/evco_a_00223.

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For a many-objective optimization problem with redundant objectives, we propose two novel objective reduction algorithms for linearly and, nonlinearly degenerate Pareto fronts. They are called LHA and NLHA respectively. The main idea of the proposed algorithms is to use a hyperplane with non-negative sparse coefficients to roughly approximate the structure of the PF. This approach is quite different from the previous objective reduction algorithms that are based on correlation or dominance structure. Especially in NLHA, in order to reduce the approximation error, we transform a nonlinearly deg

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Doerr, Benjamin, and Weijie Zheng. "Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12293–301. http://dx.doi.org/10.1609/aaai.v35i14.17459.

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Previous theory work on multi-objective evolutionary algorithms considers mostly easy problems that are composed of unimodal objectives. This paper takes a first step towards a deeper understanding of how evolutionary algorithms solve multi-modal multi-objective problems. We propose the OneJumpZeroJump problem, a bi-objective problem whose single objectives are isomorphic to the classic jump functions benchmark. We prove that the simple evolutionary multi-objective optimizer (SEMO) cannot compute the full Pareto front. In contrast, for all problem sizes n and all jump sizes k in [4..n/2-1], th

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Daly, Rich. "Parity Compromise Overcomes Most DB Objections." Psychiatric News 43, no. 19 (2008): 16. http://dx.doi.org/10.1176/pn.43.19.0016.

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Halle, Yaron, Ariel Felner, Sven Koenig, and Oren Salzman. "A Preprocessing Framework for Efficient Approximate Bi-Objective Shortest-Path Computation in the Presence of Correlated Objectives." Proceedings of the International Symposium on Combinatorial Search 18 (July 19, 2025): 65–73. https://doi.org/10.1609/socs.v18i1.35977.

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The bi-objective shortest-path (BOSP) problem seeks to find paths between start and target vertices of a graph while optimizing two conflicting objective functions. We consider the BOSP problem in the presence of correlated objectives. Such correlations often occur in real-world settings such as road networks, where optimizing two positively correlated objectives, such as travel time and fuel consumption, is common. BOSP is generally computationally challenging as the size of the search space is exponential in the number of objective functions and the graph size. Bounded sub-optimal BOSP solve

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Gong, Dunwei, Yiping Liu, and Gary G. Yen. "A Meta-Objective Approach for Many-Objective Evolutionary Optimization." Evolutionary Computation 28, no. 1 (2020): 1–25. http://dx.doi.org/10.1162/evco_a_00243.

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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

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Zhang, Han, Oren Salzman, T. K. Satish Kumar, Ariel Felner, Carlos Hernández Ulloa, and Sven Koenig. "Anytime Approximate Bi-Objective Search." Proceedings of the International Symposium on Combinatorial Search 15, no. 1 (2022): 199–207. http://dx.doi.org/10.1609/socs.v15i1.21768.

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The Pareto-optimal frontier for a bi-objective search problem instance consists of all solutions that are not worse than any other solution in both objectives. The size of the Pareto-optimal frontier can be exponential in the size of the input graph, and hence finding it can be hard. Some existing works leverage a user-specified approximation factor epsilon to compute an approximate Pareto-optimal frontier that can be significantly smaller than the Pareto-optimal frontier. In this paper, we propose an anytime approximate bi-objective search algorithm, called Anytime Bi-Objective A*-epsilon (A-

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Soni, Ras Bihari, Dr Dharamender Singh, and Dr K. C. Sharma. "Optimization of Problems with Multi-Objective Functions and their Applications in Engineering." April-May 2024, no. 43 (April 1, 2024): 18–33. http://dx.doi.org/10.55529/jecnam.43.18.33.

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Many real-world optimization issues typically have multiple competing goals. There is generally no solution in those multi-objective optimization problems that optimizes all objective functions at the same time. Rather, "efficient" in terms of all objective function’s solutions known as Pareto optimum solutions are presented. We typically have a large number of Pareto-optimal options. As a result, we must choose a final solution from among Pareto optimal solutions while considering the objective function balance; this process is known as "trade-off analysis." It is not hyperbole to state that

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Grishaeva, Lidiya Evgenievna. "Nuclear Parity — Objective Stability Factor of Multipolar World." Diplomaticheskaja sluzhba (Diplomatic Service), no. 2 (March 31, 2023): 86–103. http://dx.doi.org/10.33920/vne-01-2302-01.

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The article studies the phenomenon of nuclear parity, which serves as a guarantee of preventing a new war at the nuclear level. The author reveals the essence and traces the process of the formation of nuclear parity between the leading nuclear powers — the USA and the USSR/Russia. The author convincingly shows that at present nuclear parity is undergoing significant erosion due to the US desire to dominate in the military-strategic field, which upsets the existing balance of forces in the world and leads to destabilization of the international situation. The author reveals such concepts as "n

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Daumas-Ladouce, Federico, Miguel García-Torres, José Luis Vázquez Noguera, Diego P. Pinto-Roa, and Horacio Legal-Ayala. "Multi-Objective Pareto Histogram Equalization." Electronic Notes in Theoretical Computer Science 349 (June 2020): 3–23. http://dx.doi.org/10.1016/j.entcs.2020.02.010.

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Jaszkiewicz, Andrzej. "Many-Objective Pareto Local Search." European Journal of Operational Research 271, no. 3 (2018): 1001–13. http://dx.doi.org/10.1016/j.ejor.2018.06.009.

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Qizilbash, Mozaffar. "ON PARITY AND THE INTUITION OF NEUTRALITY." Economics and Philosophy 34, no. 1 (2017): 87–108. http://dx.doi.org/10.1017/s0266267117000281.

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Abstract:On parity views of mere addition if someone (or a group of people) is added to the world at a range of well-being levels – or ‘neutral range’ – leaving existing people unaffected, addition is on a par with the initial situation. Two distinct parity views – ‘rough equality’ and fitting-attitudes views – defend the ‘intuition of neutrality’. The first can be interpreted or adjusted so that it can rebut John Broome's objection that the neutral range is wide. The two views respond in distinct ways to two of Broome's other objections. Both views can, nonetheless, be plausibly defended agai

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Peng, Shunshun, and Taolin Guo. "Multi-Objective Service Composition Using Enhanced Multi-Objective Differential Evolution Algorithm." Computational Intelligence and Neuroscience 2023 (March 4, 2023): 1–10. http://dx.doi.org/10.1155/2023/8184367.

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In recent years, the optimization of multi-objective service composition in distributed systems has become an important issue. Existing work makes a smaller set of Pareto-optimal solutions to represent the Pareto Front (PF). However, they do not support complex mapping of the Pareto-optimal solutions to quality of service (QoS) objective space, thus having limitations in providing a representative set of solutions. We propose an enhanced multi-objective differential evolution algorithm to seek a representative set of solutions with good proximity and distributivity. Specially, we propose a dua

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Kim, Ki Sung, Kyung Su Kim, and Ki Sup Hong. "Grillage Optimization with Multiple Objectives." Key Engineering Materials 306-308 (March 2006): 517–22. http://dx.doi.org/10.4028/www.scientific.net/kem.306-308.517.

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The structural design problems are acknowledged to be commonly multicriteria in nature. The various multicriteria optimization methods are reviewed and the most efficient and easy-to-use Pareto optimal solution methods are applied to structural optimization of grillages under lateral uniform load. The result of the study shows that Pareto optimal solution methods can easily be applied to structural optimization with multiple objectives, and the designer can have a choice from those Pareto optimal solutions to meet an appropriate design environment.

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Jabs, Christoph, Jeremias Berg, Andreas Niskanen, and Matti Järvisalo. "From Single-Objective to Bi-Objective Maximum Satisfiability Solving." Journal of Artificial Intelligence Research 80 (August 2, 2024): 1223–69. http://dx.doi.org/10.1613/jair.1.15333.

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The declarative approach is key to efficiently finding optimal solutions to various types of NP-hard real-world combinatorial optimization problems. Most work on practical declarative solvers—ranging from classical integer programming to finite-domain constraint optimization and maximum satisfiability (MaxSAT)—has focused on optimization under a single objective; fewer advances have been made towards efficient declarative techniques for multi-objective optimization problems. Motivated by significant recent advances in practical solvers for MaxSAT, in this work we develop BiOptSat, an exact dec

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Hu, Tianmeng, and Biao Luo. "PA2D-MORL: Pareto Ascent Directional Decomposition Based Multi-Objective Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12547–55. http://dx.doi.org/10.1609/aaai.v38i11.29148.

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Multi-objective reinforcement learning (MORL) provides an effective solution for decision-making problems involving conflicting objectives. However, achieving high-quality approximations to the Pareto policy set remains challenging, especially in complex tasks with continuous or high-dimensional state-action space. In this paper, we propose the Pareto Ascent Directional Decomposition based Multi-Objective Reinforcement Learning (PA2D-MORL) method, which constructs an efficient scheme for multi-objective problem decomposition and policy improvement, leading to a superior approximation of Pareto

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Demirovi?, Emir, and Nicolas Schwind. "Representative Solutions for Bi-Objective Optimisation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1436–43. http://dx.doi.org/10.1609/aaai.v34i02.5501.

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Bi-objective optimisation aims to optimise two generally competing objective functions. Typically, it consists in computing the set of nondominated solutions, called the Pareto front. This raises two issues: 1) time complexity, as the Pareto front in general can be infinite for continuous problems and exponentially large for discrete problems, and 2) lack of decisiveness. This paper focusses on the computation of a small, “relevant” subset of the Pareto front called the representative set, which provides meaningful trade-offs between the two objectives. We introduce a procedure which, given a

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Liu, Yiping, Jiahao Yang, Xuanbai Ren, et al. "Multi-Objective Molecular Design Through Learning Latent Pareto Set." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19006–14. https://doi.org/10.1609/aaai.v39i18.34092.

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Molecular design inherently involves the optimization of multiple conflicting objectives, such as enhancing bio-activity and ensuring synthesizability. Evaluating these objectives often requires resource-intensive computations or physical experiments. Current molecular design methodologies typically approximate the Pareto set using a limited number of molecules. In this paper, we present an innovative approach, called Multi-Objective Molecular Design through Learning Latent Pareto Set (MLPS). MLPS initially utilizes an encoder-decoder model to seamlessly transform the discrete chemical space i

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Guo, Xiaofang, and Xiaoli Wang. "A Novel Objective Grouping Evolutionary Algorithm for Many-Objective Optimization Problems." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 06 (2019): 2059018. http://dx.doi.org/10.1142/s0218001420590181.

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The thorniest difficulties for multi-objective evolutionary algorithms (MOEAs) handling many-objective optimization problems (MaOPs) are the inefficiency of selection operators and high computational cost. To alleviate such difficulties and simplify the MaOPs, objective reduction algorithms have been proposed to remove the redundant objectives during the search process. However, those algorithms can only be applicable to specific problems with redundant objectives. Worse still, the Pareto solutions obtained by reduced objective set may not be the Pareto solutions of the original MaOPs. In this

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Filomeno Coelho, Rajan. "Bi-objective hypervolume-based Pareto optimization." Optimization Letters 9, no. 6 (2014): 1091–103. http://dx.doi.org/10.1007/s11590-014-0786-y.

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Lagouir, Marouane, Abdelmajid Badri, and Yassine Sayouti. "Solving Multi-Objective Energy Management of a DC Microgrid using Multi-Objective Multiverse Optimization." International Journal of Renewable Energy Development 10, no. 4 (2021): 911–22. http://dx.doi.org/10.14710/ijred.2021.38909.

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This paper deals with the multi-objective optimization dispatch (MOOD) problem in a DC microgrid. The aim is to formulate the MOOD to simultaneously minimize the operating cost, pollutant emission level of (NOx, SO2 and CO2) and the power loss of conversion devices. Taking into account the equality and inequality constraints of the system. Two approaches have been adopted to solve the MOOD issue. The scalarization approach is first introduced, which combines the weighted sum method with price penalty factor to aggregate objective functions and obtain Pareto optimal solutions. Whilst, the Paret

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Ikenaga, Akiko, and Sachiyo Arai. "Estimating Objective Weights of Pareto-Optimal Policies for Multi-Objective Sequential Decision-Making." Journal of Advanced Computational Intelligence and Intelligent Informatics 28, no. 2 (2024): 393–402. http://dx.doi.org/10.20965/jaciii.2024.p0393.

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Sequential decision-making under multiple objective functions includes the problem of exhaustively searching for a Pareto-optimal policy and the problem of selecting a policy from the resulting set of Pareto-optimal policies based on the decision maker’s preferences. This paper focuses on the latter problem. In order to select a policy that reflects the decision maker’s preferences, it is necessary to order these policies, which is problematic because the decision-maker’s preferences are generally tacit knowledge. Furthermore, it is difficult to order them quantitatively. For this reason, conv

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Guo, Xiaofang, Yuping Wang, and Xiaoli Wang. "Using Objective Clustering for Solving Many-Objective Optimization Problems." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/584909.

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Many-objective optimization problems involving a large number (more than four) of objectives have attracted considerable attention from the evolutionary multiobjective optimization field recently. With the increasing number of objectives, many-objective optimization problems may lead to stagnation in search process, high computational cost, increased dimensionality of Pareto-optimal front, and difficult visualization of the objective space. In this paper, a special kind of many-objective problems which has redundant objectives and which can be degenerated to a lower dimensional Pareto-optimal

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Song, Jin-Dae, and Bo-Suk Yang. "Pareto Artificial Life Algorithm for Multi-Objective Optimization." Journal of Information Technology Research 4, no. 2 (2011): 43–60. http://dx.doi.org/10.4018/jitr.2011040104.

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Most engineering optimization uses multiple objective functions rather than single objective function. To realize an artificial life algorithm based multi-objective optimization, this paper proposes a Pareto artificial life algorithm that is capable of searching Pareto set for multi-objective function solutions. The Pareto set of optimum solutions is found by applying two objective functions for the optimum design of the defined journal bearing. By comparing with the optimum solutions of a single objective function, it is confirmed that the single function optimization result is one of the spe

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Schmitt, Thomas, Tobias Rodemann, and Jürgen Adamy. "Multi-objective model predictive control for microgrids." at - Automatisierungstechnik 68, no. 8 (2020): 687–702. http://dx.doi.org/10.1515/auto-2020-0031.

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AbstractEconomic model predictive control is applied to a simplified linear microgrid model. Monetary costs and thermal comfort are simultaneously optimized by using Pareto optimal solutions in every time step. The effects of different metrics and normalization schemes for selecting knee points from the Pareto front are investigated. For German industry pricing with nonlinear peak costs, a linear programming trick is applied to reformulate the optimization problem. Thus, together with an efficient weight determination scheme, the Pareto front for a horizon of 48 steps is determined in less tha

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Younis, Adel, and Zuomin Dong. "High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm." Algorithms 15, no. 8 (2022): 279. http://dx.doi.org/10.3390/a15080279.

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The employment of conventional optimization procedures that must be repeatedly invoked during the optimization process in real-world engineering applications is hindered despite significant gains in computing power by computationally expensive models. As a result, surrogate models that require far less time and resources to analyze are used in place of these time-consuming analyses. In multi-objective optimization (MOO) problems involving pricey analysis and simulation techniques such as multi-physics modeling and simulation, finite element analysis (FEA), and computational fluid dynamics (CFD

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Parisi, Simone, Matteo Pirotta, and Marcello Restelli. "Multi-objective Reinforcement Learning through Continuous Pareto Manifold Approximation." Journal of Artificial Intelligence Research 57 (October 21, 2016): 187–227. http://dx.doi.org/10.1613/jair.4961.

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Many real-world control applications, from economics to robotics, are characterized by the presence of multiple conflicting objectives. In these problems, the standard concept of optimality is replaced by Pareto-optimality and the goal is to find the Pareto frontier, a set of solutions representing different compromises among the objectives. Despite recent advances in multi-objective optimization, achieving an accurate representation of the Pareto frontier is still an important challenge. In this paper, we propose a reinforcement learning policy gradient approach to learn a continuous approxim

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Ruppert, Jean, Marharyta Aleksandrova, and Thomas Engel. "k-Pareto Optimality-Based Sorting with Maximization of Choice and Its Application to Genetic Optimization." Algorithms 15, no. 11 (2022): 420. http://dx.doi.org/10.3390/a15110420.

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Deterioration of the searchability of Pareto dominance-based, many-objective evolutionary optimization algorithms is a well-known problem. Alternative solutions, such as scalarization-based and indicator-based approaches, have been proposed in the literature. However, Pareto dominance-based algorithms are still widely used. In this paper, we propose to redefine the calculation of Pareto-dominance. Instead of assigning solutions to non-dominated fronts, they are ranked according to the measure of dominating solutions referred to as k-Pareto optimality. In the case of probability measures, such

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Jiang, Jianhua, Jiaqi Wu, Jinmeng Luo, Xi Yang, and Zulu Huang. "MOBCA: Multi-Objective Besiege and Conquer Algorithm." Biomimetics 9, no. 6 (2024): 316. http://dx.doi.org/10.3390/biomimetics9060316.

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The besiege and conquer algorithm has shown excellent performance in single-objective optimization problems. However, there is no literature on the research of the BCA algorithm on multi-objective optimization problems. Therefore, this paper proposes a new multi-objective besiege and conquer algorithm to solve multi-objective optimization problems. The grid mechanism, archiving mechanism, and leader selection mechanism are integrated into the BCA to estimate the Pareto optimal solution and approach the Pareto optimal frontier. The proposed algorithm is tested with MOPSO, MOEA/D, and NSGAIII on

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Sienkiewicz, Ela, and Haonan Wang. "Pareto quantiles of unlabeled tree objects." Annals of Statistics 46, no. 4 (2018): 1513–40. http://dx.doi.org/10.1214/17-aos1593.

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Utyuzhnikov, Sergei, Jeremy Maginot, and Marin Guenov. "Local Pareto approximation for multi-objective optimization." Engineering Optimization 40, no. 9 (2008): 821–47. http://dx.doi.org/10.1080/03052150802086714.

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Gumus, Ergun, Zeliha Gormez, and Olcay Kursun. "Multi objective SNP selection using pareto optimality." Computational Biology and Chemistry 43 (April 2013): 23–28. http://dx.doi.org/10.1016/j.compbiolchem.2012.12.006.

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Kidanu, Rahel Amare, Maria Cunha, Elad Salomons, and Avi Ostfeld. "Improving Multi-Objective Optimization Methods of Water Distribution Networks." Water 15, no. 14 (2023): 2561. http://dx.doi.org/10.3390/w15142561.

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Water distribution network design is a complex multi-objective optimization problem and multi-objective evolutionary algorithms (MOEAs) such as NSGA II have been widely used to solve this optimization problem. However, as networks get larger, NSGA II struggles to find the diverse and uniform solutions that are critical in multi-objective optimization. This research proposes an improved version of NSGA II that uses three new-generation methods to target different regions of the Pareto front and thus increase the number of solutions in critical regions. These methods include saving an archive, l

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Zeng, Sanyou Y., Lishan S. Kang, and Lixin X. Ding. "An Orthogonal Multi-objective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints." Evolutionary Computation 12, no. 1 (2004): 77–98. http://dx.doi.org/10.1162/evco.2004.12.1.77.

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In this paper, an orthogonal multi-objective evolutionary algorithm (OMOEA) is proposed for multi-objective optimization problems (MOPs) with constraints. Firstly, these constraints are taken into account when determining Pareto dominance. As a result, a strict partial-ordered relation is obtained, and feasibility is not considered later in the selection process. Then, the orthogonal design and the statistical optimal method are generalized to MOPs, and a new type of multi-objective evolutionary algorithm (MOEA) is constructed. In this framework, an original niche evolves first, and splits int

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H., Hassan Mohamed, Fatima Daqaq, Ali Selim, José Luis Domínguez-García, and Salah Kamel. "MOIMPA: multi-objective improved marine predators algorithm for solving multi-objective optimization problems." Soft Computing 27 (July 11, 2023): 15719–40. https://doi.org/10.1007/s00500-023-08812-7.

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This paper introduces a multi-objective variant of the marine predators algorithm (MPA) called the multi-objective improved marine predators algorithm (MOIMPA), which incorporates concepts from Quantum theory. By leveraging Quantum theory, the MOIMPA aims to enhance the MPA’s ability to balance between exploration and exploitation and find optimal solutions. The algorithm utilizes a concept inspired by the Schrödinger wave function to determine the position of particles in the search space. This modification improves both exploration and exploitation, resulting in enhanced performan

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Babu, Sona, and B. S. Girish. "Pareto-optimal front generation for the bi-objective JIT scheduling problems with a piecewise linear trade-off between objectives." Operations Research Perspectives 12 (June 2024): 100299. http://dx.doi.org/10.1016/j.orp.2024.100299.

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Zhao, Menglong, Shengzhi Huang, Qiang Huang, et al. "Copula-Based Research on the Multi-Objective Competition Mechanism in Cascade Reservoirs Optimal Operation." Water 11, no. 5 (2019): 995. http://dx.doi.org/10.3390/w11050995.

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Water resources systems are often characterized by multiple objectives. Typically, there is no single optimal solution which can simultaneously satisfy all the objectives but rather a set of technologically efficient non-inferior or Pareto optimal solutions exists. Another point regarding multi-objective optimization is that interdependence and contradictions are common among one or more objectives. Therefore, understanding the competition mechanism of the multiple objectives plays a significant role in achieving an optimal solution. This study examines cascade reservoirs in the Heihe River Ba

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KRAMER, OLIVER, and HOLGER DANIELSIEK. "A CLUSTERING-BASED NICHING FRAMEWORK FOR THE APPROXIMATION OF EQUIVALENT PARETO-SUBSETS." International Journal of Computational Intelligence and Applications 10, no. 03 (2011): 295–311. http://dx.doi.org/10.1142/s1469026811003112.

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In many optimization problems in practice, multiple objectives have to be optimized at the same time. Some multi-objective problems are characterized by multiple connected Pareto-sets at different parts in decision space — also called equivalent Pareto-subsets. We assume that the practitioner wants to approximate all Pareto-subsets to be able to choose among various solutions with different characteristics. In this work, we propose a clustering-based niching framework for multi-objective population-based approaches that allows to approximate equivalent Pareto-subsets. Iteratively, the clusteri

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Farid, Mazen, Heng Siong Lim, Chin Poo Lee, and Rohaya Latip. "Scheduling Scientific Workflow in Multi-Cloud: A Multi-Objective Minimum Weight Optimization Decision-Making Approach." Symmetry 15, no. 11 (2023): 2047. http://dx.doi.org/10.3390/sym15112047.

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One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use par

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Jaroslav Janáček, Michal Koháni, Dobroslav Grygar, and René Fabricius. "Two Objective Public Service System Design Problem." Communications - Scientific letters of the University of Zilina 23, no. 4 (2021): E68—E75. http://dx.doi.org/10.26552/com.c.2021.4.e68-e75.

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The public service system serves population spread over a geographical area from a given number of service centers. One of the possible approaches to the problem with two or more simultaneously applied contradicting objectives is determination of the so-called Pareto front, i.e. set of all the feasible non-dominated solutions. The Pareto front determination represents a crucial computational deal, when a large public service system is designed using an exact method. This process complexity evoked an idea to use an evolutionary metaheuristic, which can build up a set of non-dominated solution c

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Gupta, Soumyajit, Venelin Kovatchev, Anubrata Das, Maria De-Arteaga, and Matthew Lease. "Finding Pareto trade-offs in fair and accurate detection of toxic speech." Information Research an international electronic journal 30, iConf (2025): 123–41. https://doi.org/10.47989/ir30iconf47572.

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Introduction. Optimizing NLP models for fairness poses many challenges. Lack of differentiable fairness measures prevents gradient-based loss training or requires surrogate losses that diverge from the true metric of interest. In addition, competing objectives (e.g., accuracy vs. fairness) often require making trade-offs based on stakeholder preferences, but stakeholders may not know their preferences before seeing system performance under different trade-off settings. Method. We formulate the GAP loss, a differentiable version of a fairness measure, Accuracy Parity, to provide balanced accura

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Sai Ip, Joshua Hang, Ankush Chakrabarty, Ali Mesbah, and Diego Romeres. "User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20246–54. https://doi.org/10.1609/aaai.v39i19.34230.

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Incorporating user preferences into multi-objective Bayesian optimization (MOBO) allows for personalization of the op- timization procedure. Preferences are often abstracted in the form of an unknown utility function, estimated through pair- wise comparisons of potential outcomes. However, utility-driven MOBO methods can yield solutions that are dominated by nearby solutions, as non-dominance is not enforced. Additionally, classical MOBO commonly relies on estimating the entire Pareto front to identify the Pareto-optimal solutions, which can be expensive and ignore user preferences. Here, we p

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Wei, Xin. "Multi-Objective Optimization Base on Incremental Pareto Fitness." Advanced Materials Research 1030-1032 (September 2014): 1733–36. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1733.

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A new multi-objective optimization algorithm based on incrementally Pareto fitness is proposed in this paper. To overcome the directly calculate the Pareto fitness matrix expensively, we adopt to make full use of information of last iteration at each stept to update the Parteto fitness matrix gradually. Experiments proved the highest efficiency of the new method.

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Li, Bingdong, Zixiang Di, Yongfan Lu, et al. "Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 27063–71. https://doi.org/10.1609/aaai.v39i25.34913.

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Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling complex distributions of the Pareto optimal solutions is difficult with limited function evaluations. Existing Pareto set learning algorithms may exhibit considerable instability in such expensive scenarios, leading to significant deviations between the obtained solution set and the Pareto set (PS). In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm (CDM-PSL) for expensive MOBO

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