作者: Dissonance 時間: 2025-3-21 23:48
A Two-Stage Algorithm for?Integer Multiobjective Simulation Optimizationcation. This paper proposes a two-stage fast convergent search algorithm for MDOvS. In its first stage, the multiobjective optimization problem under consideration is decomposed into several single-objective optimization subproblems, and a Pareto retrospective approximation method is used to generat作者: 豐滿中國 時間: 2025-3-22 03:55
RegEMO: Sacrificing Pareto-Optimality for?Regularity in?Multi-objective Problem-Solvinghen multiple PO solutions are to be considered for different scenarios as platform-based solutions, a common structure in them, if available, is highly desired for easier understanding, standardization, and management purposes. In this paper, we propose a modified optimization methodology to avoid c作者: medium 時間: 2025-3-22 07:45 作者: 起草 時間: 2025-3-22 12:39
Data-Driven Evolutionary Multi-objective Optimization Based on?Multiple-Gradient Descent for?Disconnh expensive objective functions. The current research is mainly developed for problems with a ‘regular’ triangle-like Pareto-optimal front (PF), whereas the performance can significantly deteriorate when the PF consists of disconnected segments. Furthermore, the offspring reproduction in the current作者: 禍害隱伏 時間: 2025-3-22 14:27
Eliminating Non-dominated Sorting from?NSGA-IIItion problems since mid-nineties. Of them, NSGA-III was designed to solve problems having three or more objectives efficiently. It is well established that with an increase in number of objectives, an increasingly large proportion of a random population stays non-dominated, thereby making only a few作者: 禍害隱伏 時間: 2025-3-22 18:45
Scalability of Multi-objective Evolutionary Algorithms for Solving Real-World Complex Optimization Pthe curse of dimensionality. This is mainly because the progression of the algorithm along successive generations is based on non-dominance relations that practically do not exist when the number of objectives is high. Also, the existence of many objectives makes the choice of a solution to the prob作者: Contend 時間: 2025-3-22 23:46
Multi-objective Learning Using HV Maximizationreferable. Intuitively, building machine learning solutions in such cases would entail providing multiple predictions that span and uniformly cover the Pareto front of all optimal trade-off solutions. We propose a novel approach for multi-objective training of neural networks to approximate the Pare作者: Ingratiate 時間: 2025-3-23 02:28
Sparse Adversarial Attack via?Bi-objective Optimizationmonstrated their vulnerability to adversarial attacks. In particular, image classifiers have shown to be vulnerable to fine-tuned noise that perturb a small number of pixels, known as sparse attacks. To generate such perturbations current works either prioritise query efficiency by allowing the size作者: floodgate 時間: 2025-3-23 08:56
Investigating Innovized Progress Operators with?Different Machine Learning Methodsuld be improved, through the intervention of Machine Learning (ML) methods. These studies have shown how . efficient search directions from the intermittent generations’ solutions, could be utilized to create pro-convergence and pro-diversity offspring, leading to better convergence and diversity, r作者: Brittle 時間: 2025-3-23 13:01
End-to-End Pareto Set Prediction with?Graph Neural Networks for?Multi-objective Facility Locationd logistics. Many mathematical and heuristic algorithms have been developed for optimizing the FLP. In addition to the transportation cost, there are usually multiple conflicting objectives in realistic applications. It is therefore desirable to design algorithms that approximate a set of Pareto sol作者: Vulnerary 時間: 2025-3-23 14:53 作者: 排名真古怪 時間: 2025-3-23 18:17 作者: 羽飾 時間: 2025-3-24 02:12
Learning to?Predict Pareto-Optimal Solutions from?Pseudo-weightstions. However, due to stochasticity involved in EMO algorithms, the uniformity in distribution of solutions cannot be guaranteed. Moreover, the follow-up decision-making activities may demand finding more solutions in specific regions on the Pareto-optimal front which may not be well-represented by作者: prostate-gland 時間: 2025-3-24 06:09
A Relation Surrogate Model for?Expensive Multiobjective Continuous and?Combinatorial Optimizationny real-world applications. Since in surrogate model assisted evolution algorithms (SAEAs), the surrogate models from the community of machine learning are usually designed from continuous problems, and they are not suitable from combinatorial problems. For this reason, we propose a convolution rela作者: arbiter 時間: 2025-3-24 09:05 作者: 混合物 時間: 2025-3-24 12:04
An Improved Fuzzy Classifier-Based Evolutionary Algorithm for?Expensive Multiobjective Optimization Ps). However, these algorithms are usually examined on test suites with unrealistically simple Pareto sets (e.g., ZDT and DTLZ test suites). Real-world MOPs usually have complicated Pareto sets, such as a vehicle dynamic design problem and a power plant design optimization problem. Such MOPs are cha作者: Confess 時間: 2025-3-24 17:47
978-3-031-27249-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Tailor 時間: 2025-3-24 19:11 作者: ACTIN 時間: 2025-3-25 01:37 作者: PURG 時間: 2025-3-25 06:27
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/317983.jpg作者: DOTE 時間: 2025-3-25 08:47
Projecting Film, Expanding Cinema,aints are easier to combine. To gain insight into the characteristics of constraint handling techniques (CHTs) for multiobjective optimization, we explore their effect independently from search methods. We regard CHTs as transformations that alter the problem landscape and visualize these modified l作者: Magisterial 時間: 2025-3-25 13:19
https://doi.org/10.1007/978-4-431-77924-7cation. This paper proposes a two-stage fast convergent search algorithm for MDOvS. In its first stage, the multiobjective optimization problem under consideration is decomposed into several single-objective optimization subproblems, and a Pareto retrospective approximation method is used to generat作者: figurine 時間: 2025-3-25 18:54
https://doi.org/10.1007/978-4-431-77922-3hen multiple PO solutions are to be considered for different scenarios as platform-based solutions, a common structure in them, if available, is highly desired for easier understanding, standardization, and management purposes. In this paper, we propose a modified optimization methodology to avoid c作者: RENIN 時間: 2025-3-25 20:29 作者: 哀悼 時間: 2025-3-26 03:47 作者: 一窩小鳥 時間: 2025-3-26 05:19
Results - fluidised bed calcium looping,tion problems since mid-nineties. Of them, NSGA-III was designed to solve problems having three or more objectives efficiently. It is well established that with an increase in number of objectives, an increasingly large proportion of a random population stays non-dominated, thereby making only a few作者: 悠然 時間: 2025-3-26 09:41 作者: 構(gòu)想 時間: 2025-3-26 12:58 作者: Instinctive 時間: 2025-3-26 19:10 作者: Substitution 時間: 2025-3-26 21:20 作者: 季雨 時間: 2025-3-27 02:51
d logistics. Many mathematical and heuristic algorithms have been developed for optimizing the FLP. In addition to the transportation cost, there are usually multiple conflicting objectives in realistic applications. It is therefore desirable to design algorithms that approximate a set of Pareto sol作者: 危機 時間: 2025-3-27 06:59
Legality Versus Efficiency of Reform, works already showed, that Hyper-Heuristics as selectors of crossover operators improve the performance of a single algorithm used on two opposing problem properties. In this paper, we present different selection mechanisms of Hyper-Heuristics, that are able to handle an expanded selection pool to 作者: 長處 時間: 2025-3-27 10:18
William E. Smith,Doras D. Hubertto replace an expensive function. In some acquisition functions, the only requirement for a regression model is the predictions. However, some other acquisition functions also require a regression model to estimate the “uncertainty” of the prediction, instead of merely providing predictions. Unfortu作者: covert 時間: 2025-3-27 13:40
Experimental Malignant Hyperthermiations. However, due to stochasticity involved in EMO algorithms, the uniformity in distribution of solutions cannot be guaranteed. Moreover, the follow-up decision-making activities may demand finding more solutions in specific regions on the Pareto-optimal front which may not be well-represented by作者: CLAIM 時間: 2025-3-27 21:42
Justin G. Stroh,Kenneth L. Rinehartny real-world applications. Since in surrogate model assisted evolution algorithms (SAEAs), the surrogate models from the community of machine learning are usually designed from continuous problems, and they are not suitable from combinatorial problems. For this reason, we propose a convolution rela作者: 離開 時間: 2025-3-27 22:08 作者: COLIC 時間: 2025-3-28 03:05 作者: 杠桿支點 時間: 2025-3-28 07:04 作者: opalescence 時間: 2025-3-28 11:48 作者: 巧辦法 時間: 2025-3-28 16:43 作者: 圓柱 時間: 2025-3-28 19:32 作者: 流浪 時間: 2025-3-28 23:12
Scalability of Multi-objective Evolutionary Algorithms for Solving Real-World Complex Optimization Positories), a machine-learning algorithm proposed by the authors. A theoretical comparison with a similar machine learning approach is made, pointing out some advantages of using the proposed algorithm using a benchmark problem designated by DTLZ5.Also, a real problem is used to show the effectiveness of the methodology. 作者: agonist 時間: 2025-3-29 05:06
A Relation Surrogate Model for?Expensive Multiobjective Continuous and?Combinatorial Optimizationbedded into a basic multiobjective evolutionary algorithm and applied to a set of continuous and combinatorial problems. The experimental results suggest that the relation model with the same settings can solve continuous and combinatorial problems, and it has an advantage in terms of problem scalability.作者: isotope 時間: 2025-3-29 10:22
0302-9743 held in Leiden, The Netherlands, during March 20-24, 2023...The 44 regular papers presented in this book were carefully reviewed and selected from 65 submissions...The papers are divided into the following topical sections: Algorithm Design and Engineering; Machine Learning and Multi-criterion Optim作者: BILIO 時間: 2025-3-29 12:35
Conference proceedings 2023s...The papers are divided into the following topical sections: Algorithm Design and Engineering; Machine Learning and Multi-criterion Optimization; Benchmarking and Performance Assessment; Indicator Design and Complexity Analysis; Applications in Real World Domains; and Multi-Criteria Decision Making and Interactive Algorithms...作者: 停止償付 時間: 2025-3-29 19:24 作者: FLOAT 時間: 2025-3-29 23:29
0302-9743 ization; Benchmarking and Performance Assessment; Indicator Design and Complexity Analysis; Applications in Real World Domains; and Multi-Criteria Decision Making and Interactive Algorithms...978-3-031-27249-3978-3-031-27250-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 有節(jié)制 時間: 2025-3-30 02:55 作者: 條約 時間: 2025-3-30 07:08 作者: 浮夸 時間: 2025-3-30 10:18 作者: Inertia 時間: 2025-3-30 13:23
Cooperative Coevolutionary NSGA-II with?Linkage Measurement Minimization for?Large-Scale Multi-objecng interactions and search the better decomposition iteratively. We evaluate our proposal on 21 benchmark functions of 500-D and 1000-D, and numerical experiments show that our proposal is quite competitive with the current popular decomposition methods.
