Results 31 to 40 of about 2,735,316 (329)
Scalable Pareto Front Approximation for Deep Multi-Objective Learning [PDF]
Industrial Conference on Data Mining, 2021 Multi-objective optimization is important for various Deep Learning applications, however, no prior multi-objective method suits very deep networks. Existing approaches either require training a new network for every solution on the Pareto front or add a
Michael Ruchte, Josif Grabocka
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New ϵ-constraint methods for multi-objective integer linear programming: A Pareto front representation approach [PDF]
European Journal of Operational Research, 2021 Dealing with multi-objective problems by using generation methods has some interesting ad-vantages since it provides the decision-maker with the complete information about the set of non-dominated points (Pareto front) and a clear overview of the problem.
Mariana Mesquita-Cunha +2 more
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EVOLUTIONARY ALGORITHM-BASED PARETO FRONT EXPLORATION FOR EFFICIENT COST-PERFORMANCE TRADEOFFS IN BIG DATA ANALYTICS [PDF]
ICTACT Journal on Soft Computing, 2023 Big data analytics often involves complex decision-making processes that require finding efficient cost-performance tradeoffs. Evolutionary algorithms (EAs) have proven to be effective in solving multi-objective optimization problems by exploring the ...
Deepak Gupta +4 more
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A new approach based on Gaussian degree of closeness for solving multi-objective optimization problems [PDF]
تصمیم گیری و تحقیق در عملیات, 2023 The Pareto set of optimal solutions resulting from solving multi-objective optimization problems, although on the one hand increases the flexibility in choosing an optimal solution according to the conditions of a system, but on the other hand, due to ...
Elham Zahiri +2 more
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Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks [PDF]
IEEE Access, 2021 Physics-informed neural networks (PINNs) have emerged as a promising deep learning method, capable of solving forward and inverse problems governed by differential equations.
F. Rohrhofer +3 more
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Improving Pareto Front Learning via Multi-Sample Hypernetworks [PDF]
AAAI Conference on Artificial Intelligence, 2022 Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem.
Ho-Ang P. Long +3 more
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On upper approximations of Pareto fronts [PDF]
Journal of Global Optimization, 2018 In one of our earlier works, we proposed to approximate Pareto fronts to multiobjective optimization problems by two-sided approximations, one from inside and another from outside of the feasible objective set, called, respectively, lower shell and upper shell. We worked there under the assumption that for a given problem an upper shell exists.
Ignacy Kaliszewski, Janusz Miroforidis
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An improved Pareto front modeling algorithm for large-scale many-objective optimization
Annual Conference on Genetic and Evolutionary Computation, 2022 A key idea in many-objective optimization is to approximate the optimal Pareto front using a set of representative non-dominated solutions. The produced solution set should be close to the optimal front (convergence) and well-diversified (diversity ...
Annibale Panichella
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Hybrid multi-objective differential evolution for multi-objective optimization of industrial polymeric materials [PDF]
Computer Methods in Materials Science, 2011 MOO of industrial case studies involving process design decisions [namely, styrene reactor, polyethylene terephthalate (PET) reactor, and low density polyethylene (LDPE) tubular reactor] is carried out using the newly developed algorithms.
Ashish M. Gujarathi, B. V. Babu
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Jurnal Matematika Integratif, 2020 Teori optimisai merupakan salah satu disiplin ilmu matematika yang banyak diterapkan dalam dunia nyata. Hampir semua masalah optimisasi di dunia nyata memiliki banyak fungsi objektif (multiobjektive) yang harus dipenuhi secara simultan dan seringkali ...
Syarifah Inayati, Rahmawati Rahmawati
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