Results 131 to 140 of about 14,697 (286)

Structured Pareto Front Representation

The paper presents a novel methodology for autonomous generation of the Pareto-Front Representation (PFR) of Linear Programming (LP) models. Following the Structured Modeling principles, the developed approach supports multiple-criteria analysis of independently developed diverse LP models.
Makowski, Marek, Granat, Janusz, Nahorski, Zbigniew, Shekhovtsov, Andrii, Ren, Hongtao, ZHAO, jinyang   +5 more
openaire   +3 more sources

Many-Objective Grasshopper Optimization Algorithm (MaOGOA): A New Many-Objective Optimization Technique for Solving Engineering Design Problems

International Journal of Computational Intelligence Systems
In metaheuristic multi-objective optimization, the term effectiveness is used to describe the performance of a metaheuristic algorithm in achieving two main goals—converging its solutions towards the Pareto front and ensuring these solutions are well ...
Kanak Kalita, Pradeep Jangir, Robert Čep, Sundaram B. Pandya, Laith Abualigah   +4 more
doaj   +1 more source

When input changes become frequent, the Pareto front shows complex changes in shape.

, 2013
We plot the Pareto front changing the parameter , which describes the typical duration between input changes. The archetypes of effectiveness and economy (marked in blue and red, respectively) are connected by the Pareto front (green) , which for any ...
Hila Sheftel (443647), Uri Alon (15569), Avi Mayo (181415), Pablo Szekely (443646)   +3 more
core   +1 more source

Multiobjective Simulation–Optimization of Crop Allocation and Water Resources in Canal Command Areas Using Genetic Algorithms

Irrigation and Drainage, EarlyView.
ABSTRACT This study develops an integrated simulation–optimization framework for sustainable crop allocation and water resource management in the Bargarh Canal Command (BCC), eastern India. Efficient irrigation allocation remains a critical challenge due to competing demands, groundwater–surface water interactions and environmental constraints ...
Priyanka Mohapatra, J. C. Paul, Sushree Sangita Dash   +2 more
wiley   +1 more source

Optimization of bioactive compounds extraction from Rosa canina L. pseudofruit through the action of two hydrolytic enzyme preparations

Journal of Chemical Technology &Biotechnology, EarlyView.
Abstract BACKROUND The pseudo‐fruit of Rosa canina L. is a rich source of bioactive compounds with antioxidant, anti‐inflammatory, anti‐cancer, anti‐diabetic, anti‐aging, and antimicrobial activities. The aim of the present study is the optimization of a green process based on the action of two hydrolytic enzyme preparations, namely Pectinex® Ultra ...
Zafeiria Lemoni, Styliani Kalantzi, Theopisti Lymperopoulou, Andromachi Tzani, Georgios Stavropoulos, Anastasia Detsi, Diomi Mamma   +6 more
wiley   +1 more source

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  

AI‐Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery

Medicinal Research Reviews, EarlyView.
ABSTRACT Artificial intelligence (AI) is transforming synthetic chemistry from task‐specific predictors into integrated platforms that unify retrosynthesis, reaction optimization, and closed‐loop robotic automation. This review highlights how AI‐assisted planning and robotic execution shorten cycle times, reduce step counts, and improve route ...
Amit Gangwal, Antonio Lavecchia
wiley   +1 more source

Efficient Fairness-Performance Pareto Front Computation

CoRR
There is a well known intrinsic trade-off between the fairness of a representation and the performance of classifiers derived from the representation. Due to the complexity of optimisation algorithms in most modern representation learning approaches, for a given method it may be non-trivial to decide whether the obtained fairness-performance curve of ...
Mark Kozdoba, Binyamin Perets, Shie Mannor   +2 more
openaire   +2 more sources

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

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