Journal articles: 'Objectifs de parité'

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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 (December 13, 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 108 ± 80,4 jours, entre la première IA et l’IA fécondante de 42,9 ± 85,2 jours, entre le vêlage et l’insémination fécondante de 152 ± 116 jours, et entre vêlages de 427 ± 122,8 jours. Un taux de réussite moyen en première IA de 54,8 % (VL) et 38,8 ± 20 % (troupeau), un index de fertilité apparent de 1,83, et 19,3 % de VL inséminées trois fois et plus (dans 16,2 ± 11 des troupeaux) ont été enregistrés. La fécondité plus que la fertilité était loin des objectifs. La race n’a eu aucun effet significatif sur la fertilité (p > 0,05), mais les performances de reproduction ont varié significativement (p < 0,05) en fonction de la taille du troupeau, de la parité, de la région d’étude, des chaleurs induites ou naturelles, et de la pratique ou non du flushing. La fécondité était plus faible chez les animaux dont la durée du tarissement dépassait 60 jours (p < 0,05). Une mise à la reproduction tardive au-delà de 80 jours post-partum a été le paramètre qui a affecté le plus les performances de reproduction des VL dans la zone d’étude.

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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 (May 7, 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 personnes ayant fait l’objet d’un dépistage et celles n’ayant pas fait l’objet d’un dépistage. Méthodes : La liste des femmes ayant accouché dans une maternité tertiaire de Montréal au Québec, entre avril 2015 et mars 2016, a été croisée avec la liste des résultats de dépistage. Les dossiers médicaux des mères ont été révisés pour les variables démographiques, prénatales et diagnostiques. Résultats : Sur 2 688 mères, 2 245 ont fait l’objet d’un dépistage au moins une fois, mais seulement 2 206 femmes avaient au moins un résultat valide pour C. trachomatis et N. gonorrhoeae le jour de l’accouchement (82,1 %, IC 95 % : 80,6 %–83,5 %). Une infection a été détectée chez 46 sur 2 206 femmes dépistées (2,1 %) : 42 présentaient une infection à C. trachomatis, deux avaient une infection à N. gonorrhoeae et deux étaient co-infectées. L’infection à C. trachomatis était plus fréquente chez les femmes de moins de 25 ans (9,8 %, IC 95 % : 6,7 %–13,8 %) que chez les femmes plus âgées (0,8 %, IC 95 % : 0,4 %–1,3 %, p < 0,001). Chaque augmentation de la parité diminuait la probabilité d’être testé (rapport de cote ajusté = 0,89, IC 95 % : 0,80 %–0,97 %, p = 0,01). Parmi celles dont le résultat initial était négatif, 35 sur 267 (13,1 %, IC 95 % : 9,3 %–17,8 %) femmes de moins de 25 ans et 122 sur 1 863 (6,6 %, IC 95 % : 5,5 %–7,8 %, p < 0,001) femmes âgées ont été retestées. Une infection subséquente a été détectée chez 4 femmes sur 35 (11 %), toutes dans le groupe des moins de 25 ans. Conclusion : Le taux sous-optimal de dépistage de C. trachomatis et N. gonorrhoeae suggère qu’actuellement, la prophylaxie oculaire universelle ne peut être abandonnée. La répétition du dépistage universel devrait être envisagée, en particulier chez les femmes de moins de 25 ans.

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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 (November 6, 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 simulation-based optimization. The observed results indicate a possibility to apply the suitable analytical model for quickly finding the acceptable approximate solution close to the Pareto optimal front.

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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 (October 18, 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 select the final solution from Pareto optimal solutions. Numerical experiments are conducted based on large-scale GPS data. Experimental results demonstrate that the proposed approach can effectively solve the problem investigated. Moreover, the comparison of single-objective and multi-objective models validates the efficiency and necessity of the proposed multi-objective model in public charging station location problems.

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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 (June 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 degenerate Pareto front into a nearly linearly degenerate Pareto front via a power transformation. In addition, an objective reduction framework integrating a magnitude adjustment mechanism and a performance metric [Formula: see text] are also proposed here. Finally, to demonstrate the performance of the proposed algorithms, comparative experiments are done with two correlation-based algorithms, LPCA and NLMVUPCA, and with two dominance-structure-based algorithms, PCSEA and greedy [Formula: see text]MOSS, on three benchmark problems: DTLZ5(I,M), MAOP(I,M), and WFG3(I,M). Experimental results show that the proposed algorithms are more effective.

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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 (May 18, 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], the global SEMO (GSEMO) covers the Pareto front in Θ((n-2k)n^k) iterations in expectation. To improve the performance, we combine the GSEMO with two approaches, a heavy-tailed mutation operator and a stagnation detection strategy, that showed advantages in single-objective multi-modal problems. Runtime improvements of asymptotic order at least k^Ω(k) are shown for both strategies. Our experiments verify the substantial runtime gains already for moderate problem sizes. Overall, these results show that the ideas recently developed for single-objective evolutionary algorithms can be effectively employed also in multi-objective optimization.

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Daly, Rich. "Parity Compromise Overcomes Most DB Objections." Psychiatric News 43, no. 19 (October 3, 2008): 16. http://dx.doi.org/10.1176/pn.43.19.0016.

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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 (March 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 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, including those non-Pareto-based ones. Particularly, it can boost the Pareto-based algorithms' ability to solve many-objective optimization problems. Due to separately evaluating the convergence and diversity performances of a solution, the traditional density-based selection criteria, for example, crowding distance, will no longer mistake a solution with poor convergence performance for a solution with low density value. By penalizing a solution in term of its convergence performance in the meta-objective space, the Pareto dominance becomes much more effective for a many-objective optimization problem. Comparative study validates the competitive performance of the proposed meta-objective approach in solving many-objective optimization problems.

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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 (July 17, 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-BOA*). A-BOA* is useful when deliberation time is limited. It first finds an approximate Pareto-optimal frontier quickly, iteratively improves it while time allows, and eventually finds the Pareto-optimal frontier. It efficiently reuses the search effort from previous iterations and makes use of a novel pruning technique. Our experimental results show that A-BOA* substantially outperforms baseline algorithms that do not reuse previous search effort, both in terms of runtime and number of node expansions. In fact, the most advanced variant of A-BOA* even slightly outperforms BOA*, a state-of-the-art bi-objective search algorithm, for finding the Pareto-optimal frontier. Moreover, given only a limited amount of deliberation time, A-BOA* finds solutions that collectively approximate the Pareto-optimal frontier much better than the solutions found by BOA*.

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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 (December 2018): 1001–13. http://dx.doi.org/10.1016/j.ejor.2018.06.009.

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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 dual strategy to adjust the usage of different creation operators, to maintain the evolutionary pressure toward the true PF. Then, we propose a reference vector neighbor search to have a fine-grained search. The proposed approach has been tested on a real-world dataset that locates a representative set of solutions with proximity and distributivity.

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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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Qizilbash, Mozaffar. "ON PARITY AND THE INTUITION OF NEUTRALITY." Economics and Philosophy 34, no. 1 (December 14, 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 against these objections.

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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 (April 3, 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 pre-computed Pareto front, computes a representative set in polynomial time, and then we show how to adapt it to the case where the Pareto front is not provided. This has three important consequences for computing the representative set: 1) does not require the whole Pareto front to be provided explicitly, 2) can be done in polynomial time for bi-objective mixed-integer linear programs, and 3) only requires a polynomial number of solver calls for bi-objective problems, as opposed to the case where a higher number of objectives is involved. We implement our algorithm and empirically illustrate the efficiency on two families of benchmarks.

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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 (September 24, 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 paper, we present a novel objective grouping evolutionary algorithm (OGEA) for general MaOPs. First, by dividing original objective set into several overlapping lower-dimensional subsets in terms of interdependence correlation information, we aim to separate the MaOPs into a number of sub-problems so that each of them can be able to preserve as much dominance structure in the original objective set as possible. Subsequently, we employ the nondominated sorting genetic algorithm II (NSGA-II) to generate Pareto solutions. Besides, instead of nondominated sorting on the whole population, a novel dual selection mechanism is proposed to choose individuals either having high ranks in subspaces or locating sparse region in the objective space for better proximity and diversity. Finally, we compare the proposed strategy with the other two classical space partition methods on benchmark DTLZ5 (I, M), DTLZ2 and a practical engineering problem. Numerical results show the proposed objective grouping algorithm can preserve more dominance structure in original objective set and achieve better quality of Pareto solutions.

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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 (August 5, 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 Pareto approach is based on the implementation of evolutionary multi-objective optimization solution. Single and multi-objective versions of multi-verse optimizer algorithm are, respectively, employed in both approaches to handle the MOOD. For each time step, a fuzzy set theory is selected to find the best compromise solution in the Pareto optimal set. The simulation results reveal that the Pareto approach achieves the best performances with a considerable decrease of 28.96 $/day in the daily operating cost, a slight reduction in the power loss of conversion devices from 419.79 kWh to 419.29 kWh, and in less computational time. While, it is noticing a small increment in the pollutant emission level from 11.54 kg/day to 12.21 kg/day, for the daily microgrid operation. This deviation can be fully covered when comparing the cost related to the treatment of these pollutants, which is only 5.55 $/day, to the significant reduction in the operating cost obtained using the Pareto approach.

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

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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 front has been investigated. Different from the works in the previous literatures, a novel metric, interdependence coefficient, which represents the nonlinear relationship between pairs of objectives, is introduced in this paper. In order to remove redundant objectives, PAM clustering algorithm is employed to identify redundant objectives by merging the less conflict objectives into the same cluster, and one of the least conflict objectives is removed. Furthermore, the potential of the proposed algorithm is demonstrated by a set of benchmark test problems scaled up to 20 objectives and a practical engineering design problem.

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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 (April 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 specific cases of Pareto set of optimum solutions.

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Schmitt, Thomas, Tobias Rodemann, and Jürgen Adamy. "Multi-objective model predictive control for microgrids." at - Automatisierungstechnik 68, no. 8 (August 27, 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 than 4 s.

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Younis, Adel, and Zuomin Dong. "High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm." Algorithms 15, no. 8 (August 7, 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), surrogate models are found to be a promising endeavor, particularly for the optimization of complex engineering design problems involving black box functions. In order to reduce the expense of fitness function evaluations and locate the Pareto frontier for MOO problems, the automated multiobjective surrogate based Pareto finder MOO algorithm (AMSP) is proposed. Utilizing data samples taken from the feasible design region, the algorithm creates three surrogate models. The algorithm repeats the process of sampling and updating the Pareto set, by assigning weighting factors to those surrogates in accordance with the values of the root mean squared error, until a Pareto frontier is discovered. AMSP was successfully employed to identify the Pareto set and the Pareto border. Utilizing multi-objective benchmark test functions and engineering design examples such airfoil shape geometry of wind turbine, the unique approach was put to the test. The cost of computing the Pareto optima for test functions and real engineering design problem is reduced, and promising results were obtained.

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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 (November 8, 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 re-definition results in an elegant and fast approximate procedure. Through experimental results on the many-objective 0/1 knapsack problem, we demonstrate the advantages of the proposed approach: (1) the approximate calculation procedure is much faster than the standard sorting by Pareto dominance; (2) it allows for achieving higher hypervolume values for both multi-objective (two objectives) and many-objective (25 objectives) optimization; (3) in the case of many-objective optimization, the increased ability to differentiate between solutions results in a better compared to NSGA-II and NSGA-III. Apart from the numerical improvements, the probabilistic procedure can be considered as a linear extension of multidimentional topological sorting. It produces almost no ties and, as opposed to other popular linear extensions, has an intuitive interpretation.

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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 approximation of the Pareto frontier in multi-objective Markov Decision Problems (MOMDPs). Differently from previous policy gradient algorithms, where n optimization routines are executed to have n solutions, our approach performs a single gradient ascent run, generating at each step an improved continuous approximation of the Pareto frontier. The idea is to optimize the parameters of a function defining a manifold in the policy parameters space, so that the corresponding image in the objectives space gets as close as possible to the true Pareto frontier. Besides deriving how to compute and estimate such gradient, we will also discuss the non-trivial issue of defining a metric to assess the quality of the candidate Pareto frontiers. Finally, the properties of the proposed approach are empirically evaluated on two problems, a linear-quadratic Gaussian regulator and a water reservoir control task.

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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 (March 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 into a group of sub-niches. Then every sub-niche repeats the above process. Due to the uniformity of the search, the optimality of the statistics, and the exponential increase of the splitting frequency of the niches, OMOEA uses a deterministic search without blindness or stochasticity. It can soon yield a large set of solutions which converges to the Pareto-optimal set with high precision and uniform distribution. We take six test problems designed by Deb, Zitzler et al., and an engineering problem (W) with constraints provided by Ray et al. to test the new technique. The numerical experiments show that our algorithm is superior to other MOGAS and MOEAs, such as FFGA, NSGAII, SPEA2, and so on, in terms of the precision, quantity and distribution of solutions. Notably, for the engineering problem W, it finds the Pareto-optimal set, which was previously unknown.

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Zhao, Menglong, Shengzhi Huang, Qiang Huang, Hao Wang, Guoyong Leng, Siyuan Liu, and Lu Wang. "Copula-Based Research on the Multi-Objective Competition Mechanism in Cascade Reservoirs Optimal Operation." Water 11, no. 5 (May 12, 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 Basin of China, with a focus on exploring the multi-objective competition mechanism among irrigation water shortage, ecological water shortage and the power generation of cascade hydropower stations. Our results can be summarized as follows: (1) the three-dimensional and two-dimensional spatial distributions of a Pareto set reveal that these three objectives, that is, irrigation water shortage, ecological water shortage and power generation of cascade hydropower stations cannot reach the theoretical optimal solution at the same time, implying the existence of mutual restrictions; (2) to avoid subjectivity in choosing limited representative solutions from the Pareto set, the long series of non-inferior solutions are adopted to study the competition mechanism. The premise of sufficient optimization suggests a macro-rule of ‘one falls and another rises,’ that is, when one objective value is inferior, the other two objectives show stronger and superior correlation; (3) the joint copula function of two variables is firstly employed to explore the multi-objective competition mechanism in this study. It is found that the competition between power generation and the other objectives is minimal. Furthermore, the recommended annual average water shortage are 1492 × 104 m3 for irrigation and 4951 × 104 m3 for ecological, respectively. This study is expected to provide a foundation for selective preference of a Pareto set and insights for other multi-objective research.

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Sienkiewicz, Ela, and Haonan Wang. "Pareto quantiles of unlabeled tree objects." Annals of Statistics 46, no. 4 (August 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 (September 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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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 (September 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 clustering process assigns the population to niches, and the multi-objective optimization process concentrates on each niche independently. Two exemplary hybridizations, rake selection and DBSCAN, as well as SMS-EMOA and kernel density clustering demonstrate that the niching framework allows enough diversity to detect and approximate equivalent Pareto-subsets.

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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 (October 1, 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 continuously in the form of an elite set. Nevertheless, the latter approach does not assure that the resulting set of solutions represents the true Pareto front of the multi-objective problem solutions. Within this paper, authors deal with both approaches to evaluate the difference between the exact and heuristic approaches.

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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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Zhao, Tongtiegang, and Jianshi Zhao. "Improved multiple-objective dynamic programming model for reservoir operation optimization." Journal of Hydroinformatics 16, no. 5 (March 27, 2014): 1142–57. http://dx.doi.org/10.2166/hydro.2014.004.

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Reservoirs are usually designed and operated for multiple purposes, which makes the multiple-objective issue important in reservoir operation. Based on multiple-objective dynamic programming (MODP), this study proposes an improved multiple-objective DP (IMODP) algorithm for reservoir operation optimization, which can be used to solve multiple-objective optimization models regardless whether the curvatures of trade-offs among objectives are concave or not. MODP retains all the Pareto-optimal solutions through backward induction, resulting in the exponential increase of computational burden with the length of study horizon. To improve the computational efficiency, this study incorporates the ranking technique into MODP and proposes an efficient IMODP algorithm. We demonstrate the effectiveness of IMODP through a hypothetical test and a real-world case. The hypothetical test includes three cases in which the trade-offs between objectives are concave, convex, and neither concave nor convex. The results show that IMODP satisfactorily captures the Pareto front for all three cases. The real-world test focuses on hydropower and analyzes the trade-offs between total energy and firm energy for Danjiangkou Reservoir. IMODP efficiently identifies the Pareto-optimal solutions and the trade-offs among objectives.

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Rossi, Mauro. "THE FITTING-ATTITUDE ANALYSIS OF VALUE RELATIONS AND THE PREFERENCES VS. VALUE JUDGEMENTS OBJECTION." Economics and Philosophy 33, no. 2 (March 13, 2017): 287–311. http://dx.doi.org/10.1017/s0266267116000286.

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Abstract:According to Wlodek Rabinowicz's (2008) fitting-attitude analysis of value relations, two items are on a par if and only if it is both permissible to strictly prefer one to the other and permissible to have the opposite strict preference. Rabinowicz's account is subject, however, to one important objection: if strict preferences involve betterness judgements, then his analysis contrasts with the intuitive understanding of parity. In this paper, I examine Rabinowicz's three responses to this objection and argue that they do not succeed. I then propose an alternative solution. I argue that the objection can be avoided if we ‘relativize’ Rabinowicz's account and define parity in terms of opposite strict preferences between two items that are only relatively permissible, rather than permissible simpliciter. I argue that this account of parity can be defended if we take seriously the distinction between sufficient and decisive reason for a preference relation. I also show that, on the basis of this distinction, we can arrive at a more extensive taxonomy of value relations than the one proposed by Rabinowicz.

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Xing, Siyuan, and Jian-Qiao Sun. "Multi-Objective Optimization of an Elastic Rod with Viscous Termination." Mathematical and Computational Applications 27, no. 6 (November 15, 2022): 94. http://dx.doi.org/10.3390/mca27060094.

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In this paper, we study the multi-objective optimization of the viscous boundary condition of an elastic rod using a hybrid method combining a genetic algorithm and simple cell mapping (GA-SCM). The method proceeds with the NSGAII algorithm to seek a rough Pareto set, followed by a local recovery process based on one-step simple cell mapping to complete the branch of the Pareto set. To accelerate computation, the rod response under impulsive loading is calculated with a particular solution method that provides accurate structural responses with less computational effort. The Pareto set and Pareto front of a case study are obtained with the GA-SCM hybrid method. Optimal designs of each objective function are illustrated through numerical simulations.

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Peri, Daniele. "Direct Tracking of the Pareto Front of a Multi-Objective Optimization Problem." Journal of Marine Science and Engineering 8, no. 9 (September 9, 2020): 699. http://dx.doi.org/10.3390/jmse8090699.

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In this paper, some methodologies aimed at the identification of the Pareto front of a multi-objective optimization problem are presented and applied. Three different approaches are presented: local sampling, Pareto front resampling and Normal Boundary Intersection (NBI). A first approximation of the Pareto front is obtained by a regular sampling of the design space, and then the Pareto front is improved and enriched using the other two above mentioned techniques. A detailed Pareto front is obtained for an optimization problem where algebraic objective functions are applied, also in comparison with standard techniques. Encouraging results are also obtained for two different ship design problems. The use of the algebraic functions allows for a comparison with the real Pareto front, correctly detected. The variety of the ship design problems allows for a generalization of the applicability of the methodology.

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Reggio, Anna, Rita Greco, Giuseppe Carlo Marano, and Giuseppe Andrea Ferro. "Stochastic Multi-objective Optimisation of Exoskeleton Structures." Journal of Optimization Theory and Applications 187, no. 3 (November 18, 2020): 822–41. http://dx.doi.org/10.1007/s10957-020-01778-8.

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AbstractIn this study, a structural optimisation problem, addressed through a stochastic multi-objective approach, is formulated and solved. The problem deals with the optimal design of exoskeleton structures, conceived as vibration control systems under seismic loading. The exoskeleton structure is assumed to be coupled to an existing primary inner structure for seismic retrofit: the aim is to limit the dynamic response of the primary structure to prevent structural damage. A non-stationary filtered Gaussian white noise stochastic process is taken as the seismic input. Design variables pertain to the mechanical properties (stiffness, damping) of the exoskeleton structure. Two concurrent and competing objective functions are introduced, in order to take into account not only safety performance but also economic cost considerations. The resulting trade-off is solved searching the Pareto front by way of a controlled elitist genetic algorithm, derived from the Non-dominated Sorting Genetic Algorithm-II. Sensitivities of Pareto fronts and Pareto optimal sets to different system parameters are finally investigated by way of a numerical application.

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Salmalian, K., N. Nariman-Zadeh, H. Gharababei, H. Haftchenari, and A. Varvani-Farahani. "Multi-objective evolutionary optimization of polynomial neural networks for fatigue life modelling and prediction of unidirectional carbon-fibre-reinforced plastics composites." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 224, no. 2 (April 1, 2010): 79–91. http://dx.doi.org/10.1243/14644207jmda260.

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In this article, evolutionary algorithms (EAs) are employed for multi-objective Pareto optimum design of group method data handling (GMDH)-type neural networks that have been used for fatigue life modelling and prediction of unidirectional (UD) carbon-fibre-reinforced plastics (CFRP) composites using input—output experimental data. The input parameters used for such modelling are stress ratio, cyclic strain energy, fibre orientation angle, maximum stress, and failure stress level in one cycle. In this way, EAs with a new encoding scheme are first presented for evolutionary design of the generalized GMDH-type neural networks, in which the connectivity configurations in such networks are not limited to adjacent layers. Second, multi-objective EAs with a new diversity preserving mechanism are used for Pareto optimization of such GMDH-type neural networks. The important conflicting objectives of GMDH-type neural networks that are considered in this work are training error (TE), prediction error (PE), and number of neurons ( N). Different pairs of these objective functions are selected for two-objective optimization processes. Therefore, optimal Pareto fronts of such models are obtained in each case, which exhibit the trade-offs between the corresponding pair of conflicting objectives and, thus, provide different non-dominated optimal choices of GMDH-type neural network model for fatigue life of UD CFRP composites. Moreover, all the three objectives are considered in a three-objective optimization process, which consequently leads to some more non-dominated choices of GMDH-type models representing the trade-offs among the TE, PE, and N (complexity of network), simultaneously. The comparison graphs of these Pareto fronts also show that the three-objective results include those of the two-objective results and, thus, provide more optimal choices for the multi-objective design of GMDH-type neural networks.

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Pei, Yan, Jun Yu, and Hideyuki Takagi. "Search Acceleration of Evolutionary Multi-Objective Optimization Using an Estimated Convergence Point." Mathematics 7, no. 2 (January 28, 2019): 129. http://dx.doi.org/10.3390/math7020129.

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We propose a method to accelerate evolutionary multi-objective optimization (EMO) search using an estimated convergence point. Pareto improvement from the last generation to the current generation supports information of promising Pareto solution areas in both an objective space and a parameter space. We use this information to construct a set of moving vectors and estimate a non-dominated Pareto point from these moving vectors. In this work, we attempt to use different methods for constructing moving vectors, and use the convergence point estimated by using the moving vectors to accelerate EMO search. From our evaluation results, we found that the landscape of Pareto improvement has a uni-modal distribution characteristic in an objective space, and has a multi-modal distribution characteristic in a parameter space. Our proposed method can enhance EMO search when the landscape of Pareto improvement has a uni-modal distribution characteristic in a parameter space, and by chance also does that when landscape of Pareto improvement has a multi-modal distribution characteristic in a parameter space. The proposed methods can not only obtain more Pareto solutions compared with the conventional non-dominant sorting genetic algorithm (NSGA)-II algorithm, but can also increase the diversity of Pareto solutions. This indicates that our proposed method can enhance the search capability of EMO in both Pareto dominance and solution diversity. We also found that the method of constructing moving vectors is a primary issue for the success of our proposed method. We analyze and discuss this method with several evaluation metrics and statistical tests. The proposed method has potential to enhance EMO embedding deterministic learning methods in stochastic optimization algorithms.

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Mintoff, Joseph. "Is the Self-Interest Theory Self-Defeating?" Dialogue 35, no. 1 (1996): 35–52. http://dx.doi.org/10.1017/s0012217300008052.

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Derek Parfit is surely right when he says, at the beginning of Reasons and Persons, that many of us want to know what we have most reason to do. Several theories attempt to answer this question, and Parfit begins his discussion with the best-known case: the Self-interest Theory, or S. When applied to actions, S claims that “(S2) What each of us has most reason to do is whatever would be best for himself, and (S3) It is irrational for anyone to do what he believes will be worse for himself” (Parfit 1984, p. 8). Objections to this theory are of many kinds, and in the first part of his book Parfit examines the objection that S is, in various ways, self-defeating. One such objection is that S implies we sometimes cannot avoid acting irrationally, but Parfit claims it is not a good objection to S that it has this implication. I disagree. The purpose of this paper is to introduce the objection in more detail (section 1), and then to argue that each of Parfit's responses to the objection is inadequate (sections 2, 3 and 4).

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Mo, Wenting, Sheng-Uei Guan, and Sadasivan Puthusserypady. "Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms." International Journal of Applied Evolutionary Computation 1, no. 2 (April 2010): 1–27. http://dx.doi.org/10.4018/jaec.2010040101.

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Many Multiple Objective Genetic Algorithms (MOGAs) have been designed to solve problems with multiple conflicting objectives. Incremental approach can be used to enhance the performance of various MOGAs, which was developed to evolve each objective incrementally. For example, by applying the incremental approach to normal MOGA, the obtained Incremental Multiple Objective Genetic Algorithm (IMOGA) outperforms state-of-the-art MOGAs, including Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA) and Pareto Archived Evolution Strategy (PAES). However, there is still an open question: how to decide the order of the objectives handled by incremental algorithms? Due to their incremental nature, it is found that the ordering of objectives would influence the performance of these algorithms. In this paper, the ordering issue is investigated based on IMOGA, resulting in a novel objective ordering approach. The experimental results on benchmark problems showed that the proposed approach can help IMOGA reach its potential best performance.

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Tomita, Kouhei, Minami Miyakawa, and Hiroyuki Sato. "Adaptive Control of Dominance Area of Solutions in Evolutionary Many-Objective Optimization." New Mathematics and Natural Computation 11, no. 02 (May 20, 2015): 135–50. http://dx.doi.org/10.1142/s1793005715400025.

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Controlling the dominance area of solutions (CDAS) relaxes the concept of Pareto dominance with an user-defined parameter S. CDAS with S < 0.5 expands the dominance area and improves the search performance of multi-objective evolutionary algorithms (MOEAs) especially in many-objective optimization problems (MaOPs) by enhancing convergence of solutions toward the optimal Pareto front. However, there is a problem that CDAS with an expanded dominance area (S < 0.5) generally cannot approximate entire Pareto front. To overcome this problem we propose an adaptive CDAS (A-CDAS) that adaptively controls the dominance area of solutions during the solutions search. Our method improves the search performance in MaOPs by approximating the entire Pareto front while keeping high convergence. In early generations, A-CDAS tries to converge solutions toward the optimal Pareto front by using an expanded dominance area with S < 0.5. When we detect convergence of solutions, we gradually increase S and contract the dominance area of solutions to obtain Pareto optimal solutions (POS) covering the entire optimal Pareto front. We verify the effectiveness and the search performance of the proposed A-CDAS on concave and convex DTLZ3 benchmark problems with 2–8 objectives, and show that the proposed A-CDAS achieves higher search performance than conventional non-dominated sorting genetic algorithm II (NSGA-II) and CDAS with an expanded dominance area.

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Son, Young Sook. "Objective Bayesian Estimation of Two-Parameter Pareto Distribution." Korean Journal of Applied Statistics 26, no. 5 (October 31, 2013): 713–23. http://dx.doi.org/10.5351/kjas.2013.26.5.713.

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CLEMENT, Maxime, Tenda OKIMOTO, and Katsumi INOUE. "Distributed Pareto Local Search for Multi-Objective DCOPs." IEICE Transactions on Information and Systems E100.D, no. 12 (2017): 2897–905. http://dx.doi.org/10.1587/transinf.2016agp0006.

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Kim, Yunyoung, Byeong Il Kim, and Joo Shin Park. "Pareto optimisation of grillage system with multi-objectives." International Journal of Modelling, Identification and Control 8, no. 3 (2009): 213. http://dx.doi.org/10.1504/ijmic.2009.029266.

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Spolaôr, Newton, Ana Carolina Lorena, and Huei Diana Lee. "Feature Selection via Pareto Multi-objective Genetic Algorithms." Applied Artificial Intelligence 31, no. 9-10 (November 26, 2017): 764–91. http://dx.doi.org/10.1080/08839514.2018.1444334.

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Ishibuchi, Hisao, Hiroyuki Masuda, and Yusuke Nojima. "Pareto Fronts of Many-Objective Degenerate Test Problems." IEEE Transactions on Evolutionary Computation 20, no. 5 (October 2016): 807–13. http://dx.doi.org/10.1109/tevc.2015.2505784.

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Schnaidt, Sebastian, Dennis Conway, Lars Krieger, and Graham Heinson. "Pareto-Optimal Multi-objective Inversion of Geophysical Data." Pure and Applied Geophysics 175, no. 6 (January 29, 2018): 2221–36. http://dx.doi.org/10.1007/s00024-018-1784-2.

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Handing Wang and Xin Yao. "Corner Sort for Pareto-Based Many-Objective Optimization." IEEE Transactions on Cybernetics 44, no. 1 (January 2014): 92–102. http://dx.doi.org/10.1109/tcyb.2013.2247594.

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Martínez-Iranzo, Miguel, Juan M. Herrero, Javier Sanchis, Xavier Blasco, and Sergio García-Nieto. "Applied Pareto multi-objective optimization by stochastic solvers." Engineering Applications of Artificial Intelligence 22, no. 3 (April 2009): 455–65. http://dx.doi.org/10.1016/j.engappai.2008.10.018.

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Journal articles on the topic 'Objectifs de parité'

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