Academic Articles & Research Papers

Multi-Objective Hybrid Optimization for Optimal Sizing of a Hybrid Renewable Power System for Home Applications

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photo...

Determination of the cost-benefit efficient interval for sponge city construction by a multi-objective optimization model

The cost-benefit is a key factor when selecting an appropriate sponge city construction scheme. The research of applying intelligent technology to find cost-benefit efficient planning and construction of sponge city is urgently required. Th...

An enhanced model predictive control method for single-stage three-phase transformerless grid-connected photovoltaic inverter

The single-stage transformerless photovoltaic (PV) topology is an attractive configuration as it offers high efficiency, low installation cost and smaller size. For such a configuration, the control algorithm should be designed to track the...

Survey of Hybrid Cloud Workflow Scheduling

In the context of data explosion,traditional cloud computing is faced with the dilemma of insufficient local cloud resources and high expansion cost.However,the newly emerging hybrid cloud combining resource-rich public cloud and data-sensi...

Bayesian ANN-Based Prediction and Multi-Objective Optimization of Tribological Behavior in Magnesium Alloy AZ91D at Elevated Temperature Using Pareto GA

This study uses an artificial neural network (ANN) model to predict the wear rate and friction coefficient of the magnesium alloy AZ91D, based on experimental data from a pin-on-disc tribometer. The model includes three essential process pa...

Energy, Exergy, Exergoeconomic and Exergoenvironmental Impact Analyses and Optimization of Various Geothermal Power Cycle Configurations

Energy, exergy, and exergoeconomic evaluations of various geothermal configurations are reported. The main operational and economic parameters of the cycles are evaluated and compared. Multi-objective optimization of the cycles is conducted...

A Heuristic Home Electric Energy Management System Considering Renewable Energy Availability

This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumpt...

A quantum fuzzy logic-enhanced evolutionary framework for energy-latency optimization in 6G Edge-IioT

Abstract The transition toward 6G-enabled Industrial Internet of Things (IIoT) requires strict Ultra-Reliable Low-Latency Communication (URLLC), creating a fundamental trade-off with energy consumption. Orchestrating resources in dense, hyp...

Optimal allocation and sizing of distributed generation for improvement of distribution feeder loss and voltage profile in the distribution network using genetic algorithm

The increasing demand for electric power, coupled with rapid urbanization, necessitates a reliable and high-quality electricity supply to meet consumer expectations. However, existing passive distribution systems are inadequate to address t...

Prioritized replay dueling double DQN for intelligent task scheduling in IoT Fog Cloud computing

Abstract Task scheduling remains a challenge in IoT-Fog-Cloud systems due to dynamic workloads and heterogeneous resources. Ineffective scheduling strategies increase latency, energy consumption, and Service Level Agreement (SLA) violations...

Multi-objective deep deterministic policy gradient for cobot trajectory planning in automotive spot welding.

Bi-Objective Portfolio Optimization Under ESG Volatility via a MOPSO-Deep Learning Algorithm

In this paper, we tackle a bi-objective optimization problem in which we aim to maximize the portfolio diversification and, at the same time, minimize the portfolio volatility, where the ESG (Environmental, Social, and Governance) informati...

High temperature Nb-Si alloys using data science: optimization of fracture toughness and high-temperature strength

Abstract High temperature Nb-Si based alloys face a critical challenge: achieving adequate room-temperature fracture toughness ( > 18 MPa·m1/2) for processing while maintaining high-temperature strength, properties that typically compete wi...

mlr: Machine Learning in R

Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensiti...

Hybrid machine learning and 3D numerical modeling-based multi-objective optimization of geosynthetic-reinforced pile-supported embankments

Efficient and reliable design of Geosynthetic Reinforced Pile-Supported (GRPS) embankments on soft ground remains challenging due to the complex nonlinear soil–structure interaction and the high computational cost of repeated numerical anal...

Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO2 emissions during pavement construction and maintenance. Additionally, the laboratory mix design process, whic...

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties ...

Multi-objective Particle Swarm Optimization Algorithm Guided by Extreme Learning Decision Network

When solving multi-objective optimization problems, particle swarm optimization algorithms usually employ preset example selection methods and search strategies, which cannot be adjusted according to specific optimization states. In the fac...