Data-driven evolutionary optimization: An overview and case studies
Most evolutionary optimization algorithms assume that the evaluation of the objective and
constraint functions is straightforward. In solving many real-world optimization problems …
constraint functions is straightforward. In solving many real-world optimization problems …
Distributed and expensive evolutionary constrained optimization with on-demand evaluation
Expensive optimization problems (EOPs) are common in industry and surrogate-assisted
evolutionary algorithms (SAEAs) have been developed for solving them. However, many …
evolutionary algorithms (SAEAs) have been developed for solving them. However, many …
A computationally efficient evolutionary algorithm for multiobjective network robustness optimization
The robustness of complex networks is of great significance. Great achievements have been
made in robustness optimization based on single measures, however, such networks may …
made in robustness optimization based on single measures, however, such networks may …
Co-design of an unmanned cable shovel for structural and control integrated optimization: A highly heterogeneous constrained multi-objective optimization algorithm
Unmanned cable shovel is an intelligence-based large machine used for excavating in open-
pit coal mines, with significant implications for the security and development of energy …
pit coal mines, with significant implications for the security and development of energy …
Transfer learning based surrogate assisted evolutionary bi-objective optimization for objectives with different evaluation times
Various multiobjective optimization algorithms have been proposed with a common
assumption that the evaluation of each objective function takes the same period of time …
assumption that the evaluation of each objective function takes the same period of time …
Key issues in real-world applications of many-objective optimisation and decision analysis
The insights and benefits to be realised through the optimisation of multiple independent,
but conflicting objectives are well recognised by practitioners seeking effective and robust …
but conflicting objectives are well recognised by practitioners seeking effective and robust …
[HTML][HTML] What if we increase the number of objectives? Theoretical and empirical implications for many-objective combinatorial optimization
The difficulty of solving a multi-objective optimization problem is impacted by the number of
objectives to be optimized. The presence of many objectives typically introduces a number …
objectives to be optimized. The presence of many objectives typically introduces a number …
Heterogeneous objectives: state-of-the-art and future research
R Allmendinger, J Knowles - … Optimization and Decision Analysis: State-of …, 2023 - Springer
Multiobjective optimization problems with heterogeneous objectives are defined as those
that possess significantly different types of objective function components (not just …
that possess significantly different types of objective function components (not just …
Constrained bi-objective surrogate-assisted optimization of problems with heterogeneous evaluation times: Expensive objectives and inexpensive constraints
In the past years, a significant amount of research has been done in optimizing
computationally expensive and time-consuming objective functions using various surrogate …
computationally expensive and time-consuming objective functions using various surrogate …
Handling constrained multi-objective optimization problems with heterogeneous evaluation times: proof-of-principle results
Most real-world optimization problems consist of multiple objectives to be optimized and
multiple constraints to be satisfied. Moreover, the performance assessment of the objective …
multiple constraints to be satisfied. Moreover, the performance assessment of the objective …