Neural architecture search as multiobjective optimization benchmarks: Problem formulation and performance assessment
The ongoing advancements in network architecture design have led to remarkable
achievements in deep learning across various challenging computer vision tasks …
achievements in deep learning across various challenging computer vision tasks …
Learning-aided evolution for optimization
Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …
solving. Similarly, computer scientists have made great efforts to design artificial neural …
A new many-objective evolutionary algorithm based on generalized Pareto dominance
In the past several years, it has become apparent that the effectiveness of Pareto-dominance-
based multiobjective evolutionary algorithms deteriorates progressively as the number of …
based multiobjective evolutionary algorithms deteriorates progressively as the number of …
A survey of normalization methods in multiobjective evolutionary algorithms
A real-world multiobjective optimization problem (MOP) usually has differently scaled
objectives. Objective space normalization has been widely used in multiobjective …
objectives. Objective space normalization has been widely used in multiobjective …
Multi-layer distributed multi-objective consensus algorithm for multi-objective economic dispatch of large-scale multi-area interconnected power systems
L Yin, Z Sun - Applied Energy, 2021 - Elsevier
Traditional multi-objective economic dispatch problems all apply centralized multi-objective
optimization methods. However, with the rapid development of smart grids, issues such as …
optimization methods. However, with the rapid development of smart grids, issues such as …
A two-stage evolutionary algorithm for large-scale sparse multiobjective optimization problems
There is evidence that many real-world applications can be characterized as sparse
multiobjective problems (SMOPs), where most variables of their Pareto optimal solutions are …
multiobjective problems (SMOPs), where most variables of their Pareto optimal solutions are …
A steady-state algorithm for solving expensive multiobjective optimization problems with nonparallelizable evaluations
Expensive multiobjective optimization problems (EMOPs) refer to those wherein evaluation
of each candidate solution incurs a significant cost. To solve such problems within a limited …
of each candidate solution incurs a significant cost. To solve such problems within a limited …
Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug
Due to its cost-effectiveness, convenience, and high patient adherence, oral drug
administration normally remains the preferred approach. Yet, the effective delivery of …
administration normally remains the preferred approach. Yet, the effective delivery of …
[HTML][HTML] Multicriteria Optimization Techniques for Understanding the Case Mix Landscape of a Hospital
Various medical and surgical units operate in a typical hospital and to treat their patients
these units compete for infrastructure like operating rooms (OR) and ward beds. How that …
these units compete for infrastructure like operating rooms (OR) and ward beds. How that …
Effects of corner weight vectors on the performance of decomposition-based multiobjective algorithms
Recently, it was demonstrated that a decomposition-based multiobjective evolutionary
algorithm with a pre-specified weight vector set cannot find a uniformly-distributed solution …
algorithm with a pre-specified weight vector set cannot find a uniformly-distributed solution …