Tag: Multi-objective optimization

On a primary synthetic benchmark, landscape-aware ant colony optimization reduced modeled conflict by 31.1% for a 13.9% increase in route ...

A response-surface-guided neural network predicted a balanced silica-fume and nano-silica formulation for coral aggregate concrete, reducing prediction error without additional ...

A computational comparison found that two moderately thick aluminum L-shaped fins gave the best tested charging–discharging balance for a rectangular ...

A neural-network surrogate paired with differential evolution reduced model-predicted EV battery thermal risk by 94.8% across charging rates from 0.5C ...

A multi-objective reinforcement-learning system reduced cobot trajectory error, collisions, cycle time, and energy use in a controlled simulation benchmark, while ...

A neural network predictive control framework that jointly optimizes thermal comfort, daylighting, energy use, and life-cycle cost cut a school ...

A new hybrid Pareto-safe distributional reinforcement learning scheduler balances latency, energy, SLA, and load balance in cloud task allocation while ...

A new DDPG-based scheduling framework called SPARCD uses vehicle position prediction, action masking, and state filling to cut task interruption ...

A new review shows how combining design of experiments with machine learning can transform hydrothermal carbonization from empirical tuning into ...

Researchers converted HDPE plastic waste into pyrolysis oil, blended it with diesel, and used machine learning with a genetic algorithm ...

A new two-stage framework couples improved wind and solar power forecasting with multi-objective shared energy storage planning, achieving a 90.3 ...

A new AI decision engine combines BERT-based reading of disruption reports with a fuzzy-controlled bat algorithm to cut supply chain ...

A CFD-driven multi-objective optimization of 192 shell-and-tube heat exchanger configurations shows how Marun furnace exhaust heat at 973 K can ...

Researchers have developed an interpretable deep reinforcement learning framework that schedules wind turbine maintenance to maximize power generation and minimize ...

Researchers in India have developed a hybrid AI framework combining AHP and an opposition-based NSGA-III algorithm that simultaneously optimizes construction ...

Researchers have combined non-negative matrix factorization with multi-objective particle swarm optimization to create a community detection algorithm that achieves perfect ...

Researchers at Fujian Normal University have developed a color image watermarking method in the discrete quaternionic quadratic phase Fourier transform ...

Researchers have combined conditional diffusion models with graph neural networks to generate coordinated energy schedules for building clusters, achieving a ...

A three-year field study in the North China Plain identifies optimal irrigation and nitrogen ranges that simultaneously raise grain yield, ...

Researchers combined tapered main channels with wavy sub-channels in a PEM fuel cell and used a physics-informed surrogate model with ...