Results 181 to 190 of about 31,789 (298)

Evolving dynamic multiple-objective optimization problems with objective replacement

, 2005
This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution.
Mo, W, Guan, SU, Chen, Q
core  

Machine Learning‐Driven Predictive Modeling and Multi‐Objective Exploration of Oxaliplatin‐Loaded Nanocarriers for Enhanced Loading and Encapsulation Efficiency

Materials Genome Engineering Advances, EarlyView.
Integrating ML and multi‐objective optimization enabled efficient, accurate design of nanocarriers with optimized loading and encapsulation efficiencies. ABSTRACT Oxaliplatin (OXA), a critical third‐generation platinum chemotherapeutic, is significantly limited by suboptimal loading capacity and encapsulation efficiency in nanoparticle‐based delivery ...
Abbas Rahdar, Maryam Shirzad, Sonia Fathi‐Karkan, M. Ali Aboudzadeh   +3 more
wiley   +1 more source

Multi-Objective Optimization of Damage Volume and CO₂ Consumption for High-Pressure Liquid CO₂ Jet Impact on Hydroxyl-Terminated Polybutadiene Propellant. [PDF]

Materials (Basel)
Zhang Z, Jiang D, Bai Y, Zhang H, Ding Y.   +4 more
europepmc   +1 more source

Pareto Front Shape-Agnostic Pareto Set Learning in Multi-Objective Optimization

Pareto set learning (PSL) is an emerging approach for acquiring the complete Pareto set of a multi-objective optimization problem. Existing methods primarily rely on the mapping of preference vectors in the objective space to Pareto optimal solutions in ...
Kou, Wei-Bin, Zhang, Jinyuan, Ye, Rongguang, Ishibuchi, Hisao, Chen, Longcan   +4 more
core  

Physics‐Informed Generative Machine Learning for Designing Crack‐Free γ′‐Strengthened Ni‐Based Superalloys for Laser Powder Bed Fusion

Materials Genome Engineering Advances, EarlyView.
This research proposes a physics‐informed generative machine learning framework to design SHA800, a crack‐free γ′‐strengthened nickel‐based superalloy for laser powder bed fusion, achieving a 43% γ′ volume fraction and 587 HV0.2 hardness. ABSTRACT Fabricating γ′‐strengthened nickel‐based superalloys via laser powder bed fusion (LPBF) faces significant ...
Kai Guo, Dongwei Guan, Tao Hu, Yaqing Hou, Xiaoqun Li, Zhongnan Bi, Songzhe Xu, Hao Yu, Wei Xu, Weidong Xuan, Chaoyue Chen, Zhongming Ren   +11 more
wiley   +1 more source

Aerodynamic Optimization of Relay Nozzle Using a Chebyshev KAN Surrogate Model Integration and an Improved Multi-Objective Red-Billed Blue Magpie Optimizer. [PDF]

Biomimetics (Basel)
Shen M, Zhang Z, Qin G, Zhou D, Du L, Yu L.   +5 more
europepmc   +1 more source

Signal and Contrast Optimization With Predicted Excitations (SCOPE) for Accelerating Large FOV Body Imaging at UHF

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Large FOV turbo‐spin‐echo (TSE) imaging at ultra‐high field (UHF) remains challenging due to B1+ inhomogeneity and peak specific absorption rate (pSAR) limitations. This work presents a new time‐interleaved acquisition of modes (TIAMO) framework called SCOPE (Signal and Contrast Optimization with Predicted Excitations), which overcomes
Tobey D. Haluptzok, Simon Schmidt, Gregory J. Metzger   +2 more
wiley   +1 more source

Efficient EEG channel-and-frequency-band selection for epileptic seizure classification using multi-objective optimization. [PDF]

Front Neurol
Chen W, Lei X, Guo H, Zhuang L.
europepmc   +1 more source

A Novel Two‐Stage Flexible Flow Shop Batch Scheduling Model for Grinding Workshops

Naval Research Logistics (NRL), EarlyView.
ABSTRACT The growing need for data storage in data centers has increased the demand for mechanical hard disks due to their low cost and high reliability. Aluminum substrates are the most popular base plates for mechanical hard disks because of their high hardness and low cost.
Jun Xu, Xuanru Pan, Chao‐Bo Yan, Guo‐Qiang Fan, Zhixin Liu   +4 more
wiley   +1 more source

Pareto-optimized stacked ensemble machine learning framework for predicting bearing capacity of driven piles from static load test data. [PDF]

Sci Rep
Abdellatief M, ElNemr A, Altahrany A.
europepmc   +1 more source