標(biāo)題: Titlebook: Handbook of Formal Optimization; Anand J. Kulkarni,Amir H. Gandomi Living reference work 20230th edition Engineering Optimization.Nature [打印本頁] 作者: fumble 時(shí)間: 2025-3-21 17:16
書目名稱Handbook of Formal Optimization影響因子(影響力)
書目名稱Handbook of Formal Optimization影響因子(影響力)學(xué)科排名
書目名稱Handbook of Formal Optimization網(wǎng)絡(luò)公開度
書目名稱Handbook of Formal Optimization網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Handbook of Formal Optimization被引頻次
書目名稱Handbook of Formal Optimization被引頻次學(xué)科排名
書目名稱Handbook of Formal Optimization年度引用
書目名稱Handbook of Formal Optimization年度引用學(xué)科排名
書目名稱Handbook of Formal Optimization讀者反饋
書目名稱Handbook of Formal Optimization讀者反饋學(xué)科排名
作者: Desert 時(shí)間: 2025-3-21 21:24 作者: 公司 時(shí)間: 2025-3-22 02:16 作者: Cardiac-Output 時(shí)間: 2025-3-22 06:00 作者: Nebulizer 時(shí)間: 2025-3-22 12:46 作者: 政府 時(shí)間: 2025-3-22 16:28 作者: ACTIN 時(shí)間: 2025-3-22 17:28
https://doi.org/10.1007/978-3-662-24625-2emotions using machine learning classification algorithms. The dataset used in this work is generated using the benchmark CASE (Sharma K, Castellini C, van den Broek EL, Albu-Schaeffer A, Schwenker F (2019) A dataset of continuous affect annotations and physiological signals for emotion analysis. J 作者: 頑固 時(shí)間: 2025-3-22 23:02
Studienbücher zur SozialwissenschaftHowever, PCA does not consider class information, which can be crucial for classification tasks. On the other hand, linear discriminant analysis (LDA) considers class information. It aims to find a projection that maximizes the separation between different classes while minimizing the variance withi作者: Servile 時(shí)間: 2025-3-23 02:39
https://doi.org/10.1007/978-3-642-74327-6s, and considering economic factors. To this end, this paper presents the dynamic model of the PTO systems and also the capability of optimization algorithms for adjusting the PTO parameters. In addition, this paper provides an overview of the current state of research on the robust optimization of 作者: 旁觀者 時(shí)間: 2025-3-23 09:15
https://doi.org/10.1007/978-3-322-88306-3g process. Thus, the DRL was tailored to solve the PLP with and without taking into account the practical stability constraints. The proposed approach was implemented, and computational experimentation was conducted on 16 Benchmark instances from the literature. Numerical results demonstrate that th作者: 單調(diào)女 時(shí)間: 2025-3-23 13:26 作者: 制造 時(shí)間: 2025-3-23 14:38 作者: Flirtatious 時(shí)間: 2025-3-23 19:32
https://doi.org/10.1007/978-3-662-42428-5esign is solved with socio-inspired AI-based metaheuristics referred to as Cohort Intelligence algorithm, and solutions are compared with genetic algorithm (GA), simulated annealing (SA), and Monte Carlo simulations. Results show an improvement of 59%, 11%, and 54% for achievement of goals when comp作者: Visual-Acuity 時(shí)間: 2025-3-23 22:53 作者: 緩和 時(shí)間: 2025-3-24 04:39
https://doi.org/10.1007/978-3-663-20249-3nalysis”). Nine structural materials are evaluated for the optimal gear material, along with evaluation criteria such as surface and core harnesses, surface and bending fatigue limits, and ultimate tensile strength. The PSI (“preference selection index”) approach is employed to weight the criteria. 作者: DRAFT 時(shí)間: 2025-3-24 07:43 作者: 身心疲憊 時(shí)間: 2025-3-24 14:18 作者: 推測 時(shí)間: 2025-3-24 15:47 作者: BYRE 時(shí)間: 2025-3-24 22:08 作者: 從屬 時(shí)間: 2025-3-25 00:27 作者: Ballerina 時(shí)間: 2025-3-25 03:50 作者: 經(jīng)典 時(shí)間: 2025-3-25 10:21 作者: 符合規(guī)定 時(shí)間: 2025-3-25 13:49 作者: Feature 時(shí)間: 2025-3-25 19:19 作者: 通便 時(shí)間: 2025-3-25 23:59 作者: Agronomy 時(shí)間: 2025-3-26 00:17
Optimization of Concrete Chimneys Considering Random Underground Blast and Temperature Effects, of uncertainty. A robust design optimization (RDO) approach is noted to be suitable to consider the effects of uncertainty in the design of chimney under such random loading conditions which is the subject of the present study. To make the RDO procedure computationally efficient, a cumulative distr作者: 敵手 時(shí)間: 2025-3-26 06:23
Gear Material Selection Using an Integrated PSI-MOORA Method,nalysis”). Nine structural materials are evaluated for the optimal gear material, along with evaluation criteria such as surface and core harnesses, surface and bending fatigue limits, and ultimate tensile strength. The PSI (“preference selection index”) approach is employed to weight the criteria. 作者: 受傷 時(shí)間: 2025-3-26 10:52 作者: 慌張 時(shí)間: 2025-3-26 14:54 作者: exceed 時(shí)間: 2025-3-26 17:26 作者: induct 時(shí)間: 2025-3-27 00:54 作者: Halfhearted 時(shí)間: 2025-3-27 02:06 作者: Self-Help-Group 時(shí)間: 2025-3-27 06:48
Robust Optimization of Discontinuous Loss Functions, Alternatively, the loss and activation functions can be the source of discontinuities. This chapter gives illustrative examples of some of the origins of discontinuous loss functions and some basic strategies for exploiting gradients to optimize loss functions induced with discretization and sampling errors, i.e., gradient-only optimization.作者: 監(jiān)禁 時(shí)間: 2025-3-27 11:03
Commonly Used Static and Dynamic Single-Objective Optimization Benchmark Problems,al and eight multimodal ones, and several dynamic benchmark generators have been reviewed. Covering both categories can help researchers understand the differences between dynamic and static benchmark problems.作者: frugal 時(shí)間: 2025-3-27 14:53
Neural Networks and Deep Learning,and concept of parameter selection in deep learning are discussed. In the end, the performance of deep neural models is presented, and classic deep learning models, including stacking automatic encoders, convolutional neural networks, deep probabilistic neural networks, and generative adversarial networks, are introduced.作者: 瘙癢 時(shí)間: 2025-3-27 20:48
https://doi.org/10.1007/978-3-663-05618-8s the algorithm for solving combinatorial optimization problems, such as the CCVRP. The D-CS implementation is described in detail, and the algorithm is evaluated on well-known CCVRP benchmark instances. The behavior of the D-CS is discussed, in an extensive sensitivity analysis.作者: exquisite 時(shí)間: 2025-3-27 22:24 作者: Adulterate 時(shí)間: 2025-3-28 04:39 作者: Pillory 時(shí)間: 2025-3-28 07:57 作者: 有斑點(diǎn) 時(shí)間: 2025-3-28 11:25
Living reference work 20230th editionharts/pseudocodes, illustrations, problems and application(s), results and critical discussions, flowcharts/pseudocodes, etc. The editors have brought together almost every aspect of this enormous field of formal optimization such as mathematical and Bayesian optimization, neural networks and deep l作者: stressors 時(shí)間: 2025-3-28 16:07
Living reference work 20230th editionarm-based optimization, among others. The handbook serves as a complete reference discussing a wide aspect of formal optimization methods. This handbook will be useful for experts as well as non-specialists as they will find the material stimulating. The book covers research trends, challenges, and prospective topics as well..作者: 創(chuàng)造性 時(shí)間: 2025-3-28 19:04
https://doi.org/10.1007/978-3-663-02000-4est-known solutions and existing algorithms for performance analysis using the benchmark dataset. Analysis has been performed using measures like route cost, standard deviation, and percentage variation in length. The results have also been statistically verified for their significance.作者: 機(jī)構(gòu) 時(shí)間: 2025-3-29 00:21
https://doi.org/10.1007/978-3-642-56062-0ity of the obtained solutions can be proven when the neighborhood size is maximal and with unbounded tree search. Finally, we perform experiments on several instances from the Computational Protein Design (CDP) problem, showing the practical benefit of our VNS-based approaches.作者: 異教徒 時(shí)間: 2025-3-29 04:22 作者: 刺耳 時(shí)間: 2025-3-29 10:59 作者: 忙碌 時(shí)間: 2025-3-29 13:40 作者: Corporeal 時(shí)間: 2025-3-29 19:30
Deep Learning for Solving Loading, Packing, Routing, and Scheduling Problems, detail. The studies selected show that increasing attention is being given to DL to solve combinatorial optimization problems over the years. Precisely, the Q-learning and policy gradients are the most used algorithms, and the scheduling and loading problems are, respectively, the most and the least handled.作者: MIRTH 時(shí)間: 2025-3-29 23:12 作者: 易于 時(shí)間: 2025-3-30 00:53
A Discrete Cuckoo Search Algorithm for the Cumulative Capacitated Vehicle Routing Problem,e population properties of the cuckoo search algorithm, and its ability to progressively improve the solutions’ quality, with the strong effectiveness of greedy and heuristic algorithms. Unlike the original CS which was designed for solving continuous optimization problems, this implementation adapt作者: Bucket 時(shí)間: 2025-3-30 05:45 作者: 羊齒 時(shí)間: 2025-3-30 11:30 作者: AXIS 時(shí)間: 2025-3-30 15:21 作者: temperate 時(shí)間: 2025-3-30 19:20
Solving Vehicle Routing Problem Using a Hybridization of ,-Based Ant Colony Optimization and Fireflearch domain. Though many variations of classical VRP are being developed, there is still the need for developing algorithms to improve solutions for VRP. A hybrid gain-based ant colony optimization-firefly algorithm (GACO-FA) has been proposed to deal with VRP. A global search is initially performe作者: chance 時(shí)間: 2025-3-31 00:31
Impact of Local Search in the Memetic Particle Swarm Optimization,ation capability due to the implementation of local search algorithms helping the population-based algorithms. They were used to solve several theoretical and practical optimization problems. Despite this, there is still a need to study further these algorithms’ behavior and what makes an algorithm 作者: jaunty 時(shí)間: 2025-3-31 03:56
Classification of Emotions in Ambient Assisted Living Environment using Machine Learning Approaches are in need. The AAL helps a person’s health and well-being by systematically managing their daily routines and activities. AAL’s goals include allowing individuals to live independently in a preferred setting, keeping an eye on their health, and maintaining privacy and security. The individuals re作者: 哪有黃油 時(shí)間: 2025-3-31 08:38 作者: 整潔 時(shí)間: 2025-3-31 09:17
Variable Neighborhood Search for Cost Function Networks,h over-constrained problems as well as preferences between solutions. Most solving approaches for CFNs rely on complete tree search methods. Few attempts have been done to use local search approaches to solve CFNs. This chapter investigates the use of Variable Neighborhood Search (VNS) for CFNs. We 作者: 使長胖 時(shí)間: 2025-3-31 14:24
Neural Networks and Deep Learning,work constituting the human brain so that the computer can learn and make decisions like a human. On the other hand, deep learning indicates a neural network with more than three layers. Deep neural networks are capable of extracting higher-level features from the raw data to solve complicated optim作者: CODE 時(shí)間: 2025-3-31 19:13 作者: vascular 時(shí)間: 2025-4-1 01:32
Deep Learning for Solving Loading, Packing, Routing, and Scheduling Problems,ed by an agent through its actions in accordance with the state of the environment. Deep learning has proved to be efficient in solving complex optimization problems. In this study, we investigate the use of deep learning (DL) to solve combinatorial optimization problems related to scheduling, packi作者: abracadabra 時(shí)間: 2025-4-1 02:14
Solving the Pallet Loading Problem with Deep Reinforcement Learning,rs as one of the most challenging cutting and packing optimization problems. To solve the PLP, several state-of-the-art Operations Research techniques have been used in the existing literature. This study evaluates the capacity of Deep Reinforcement Learning (DRL), one of the trendiest techniques of作者: 迎合 時(shí)間: 2025-4-1 06:04
