Multi-objective optimization for improved project management: Current status and future directions
K Guo, L Zhang - Automation in Construction, 2022 - Elsevier
To realize project management improvement, many different objectives have to be
considered simultaneously due to the nature of complexity and uncertainty in construction …
considered simultaneously due to the nature of complexity and uncertainty in construction …
Evolutionary dynamic multi-objective optimisation: A survey
Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly
growing area of investigation. EDMO employs evolutionary approaches to handle multi …
growing area of investigation. EDMO employs evolutionary approaches to handle multi …
Digital twin-enabled dynamic scheduling with preventive maintenance using a double-layer Q-learning algorithm
Q Yan, H Wang, F Wu - Computers & Operations Research, 2022 - Elsevier
Dynamic scheduling methods are essential and critical to manufacturing systems because of
uncertain events in the production process, such as new job insertions, order cancellations …
uncertain events in the production process, such as new job insertions, order cancellations …
Deep reinforcement learning based adaptive operator selection for evolutionary multi-objective optimization
Evolutionary algorithms (EAs) have become one of the most effective techniques for multi-
objective optimization, where a number of variation operators have been developed to …
objective optimization, where a number of variation operators have been developed to …
A survey on learnable evolutionary algorithms for scalable multiobjective optimization
Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
AS-NAS: Adaptive scalable neural architecture search with reinforced evolutionary algorithm for deep learning
Neural architecture search (NAS) is a challenging problem in the design of deep learning
due to its nonconvexity. To address this problem, an adaptive scalable NAS method (AS …
due to its nonconvexity. To address this problem, an adaptive scalable NAS method (AS …
A new prediction strategy for dynamic multi-objective optimization using Gaussian Mixture Model
Dynamic multi-objective optimization problems (DMOPs), in which the environments change
over time, have attracted many researchers' attention in recent years. Since the Pareto set …
over time, have attracted many researchers' attention in recent years. Since the Pareto set …
[HTML][HTML] A comprehensive review of bio-inspired optimization algorithms including applications in microelectronics and nanophotonics
The application of artificial intelligence in everyday life is becoming all-pervasive and
unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired …
unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired …
Dynamic multi-objective evolutionary algorithm based on knowledge transfer
L Wu, D Wu, T Zhao, X Cai, L Xie - Information Sciences, 2023 - Elsevier
Dynamic multi-objective optimization problems (DMOPs) are mainly reflected in objective
changes with changes in the environment. To solve DMOPs, a transfer learning (TL) …
changes with changes in the environment. To solve DMOPs, a transfer learning (TL) …
Adaptive auxiliary task selection for multitasking-assisted constrained multi-objective optimization [feature]
Solving constrained multi-objective optimization problems (CMOPs) is one of the most
popular research topics in the multi-objective optimization community. Various approaches …
popular research topics in the multi-objective optimization community. Various approaches …