Results 111 to 120 of about 4,635,823 (252)

Distributed AutoML framework for multi‐objective optimization of concrete crack segmentation models

Structural Concrete, EarlyView.
Abstract Monitoring cracks in concrete surfaces is essential for structural safety. While machine vision techniques have received significant interest in this domain, selecting optimal models and tuning hyperparameters remain challenging. This paper proposes a Distributed Automated Machine Learning (AutoML) framework for efficiently designing and ...
Armin Dadras Eslamlou, Shiping Huang, Belén Riveiro, M. Z. Naser   +3 more
wiley   +1 more source

Multi-Objective Optimization for Coordinated Day-Ahead Scheduling Problem of Integrated Electricity-Natural Gas System With Microgrid

IEEE Access, 2020
This paper presents a multi-objective optimization algorithm for coordinated day-ahead scheduling problem of integrated electricity-natural gas system with microgrid (IENGS-M).
J. H. Zheng, C. Q. Wu, J. Huang, Y. Liu, Q. H. Wu   +4 more
doaj   +1 more source

Evolving dynamic multiple-objective optimization problems with objective replacement [PDF]

, 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.
Chen, Q, Guan, SU, Mo, W
core  

Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou, Juan Zou, Gan Ruan, Yuan Liu, Yizhang Xia   +4 more
wiley   +1 more source

Multi-objective optimization of construction project management based on an improved genetic algorithm [PDF]

Archives of Civil Engineering
In construction project management, it is crucial to consider multiple objectives, such as duration and cost, to develop an optimal plan. This paper established a multi-objective optimization model, taking into account the construction period, cost ...
Xiaoyan Dai, Wanwan Xia, Yingwen Xu
doaj   +1 more source

Multi-Objective Self-Organizing Migrating Algorithm: Sensitivity on Controlling Parameters [PDF]

, 2013
In this paper, we investigate the sensitivity of a novel Multi-Objective Self-Organizing Migrating Algorithm (MOSOMA) on setting its control parameters.
Drinovsky, J., Kadlec, P., Raida, Zbyněk   +2 more
core   +1 more source

3D print orientation optimization and comparative analysis of NSGA-II versus NSGA-II with Q-learning

International Journal on Smart Sensing and Intelligent Systems
Abstract This study optimizes 3D print orientation to minimize support material, printing time, and surface roughness using non-dominated sorting genetic algorithm II (NSGA-II). Traditional NSGA-II can stagnate due to static parameters; thus, integration with Q-learning dynamically adjusts these parameters based on rewards. Q-learning, a
G. Bilowo, B. Hardjono
openaire   +1 more source

MOEA/D‐based multi‐row facility layout optimisation method with discontinuity perceiving of the Pareto front

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract In a multi‐row facility layout problem (MRFLP), facilities are arranged in more than one row under the limited layout area. Considering different layout factors, various extensions of MRFLP have been modelled. However, the orientation of input/output (I/O) point in a facility, as a key factor that plays a direct impact on flow cost, is seldom ...
Yinan Guo, Yao Huang, Jian Cheng, Xiaoyu Ban, Xiaoxiao Zhao, Tianyu Liu   +5 more
wiley   +1 more source

Multi‐Objective Optimisation Framework for Heterogeneous Federated Learning

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server. Despite rapid progress, federated learning still faces several unsolved challenges. Specifically, communication costs and system heterogeneity, such as nonidentical data distribution,
Jamshid Tursunboev, Vikas Palakonda, Il‐Min Kim, Sunghwan Moon, Jae‐Mo Kang   +4 more
wiley   +1 more source

A Bi-Objective Reverse Logistics Network Design Under the Emission Trading Scheme

IEEE Access, 2019
In general, reverse logistics network design has been driven by a need to reduce costs and to improve customer service without considering its environmental impact.
Qunli Yuchi, Nengmin Wang, Shuang Li, Zhen Yang, Bin Jiang   +4 more
doaj   +1 more source