Boosting data-driven evolutionary algorithm with localized data generation
By efficiently building and exploiting surrogates, data-driven evolutionary algorithms
(DDEAs) can be very helpful in solving expensive and computationally intensive problems …
(DDEAs) can be very helpful in solving expensive and computationally intensive problems …
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 …
Dual-fuzzy-classifier-based evolutionary algorithm for expensive multiobjective optimization
Multiobjective evolutionary algorithms (MOEAs) have been widely used to solve
multiobjective optimization problems (MOPs). Conventional MOEAs usually require a large …
multiobjective optimization problems (MOPs). Conventional MOEAs usually require a large …
A surrogate-assisted evolutionary algorithm with hypervolume triggered fidelity adjustment for noisy multiobjective integer programming
Although surrogate-assisted evolutionary algorithms (SAEAs) have been widely developed
to address computationally expensive multi-objective optimization problems (MOPs), they …
to address computationally expensive multi-objective optimization problems (MOPs), they …
Interactive multiobjective optimization of an extremely computationally expensive pump design problem
J Burkotová, P Aghaei Pour, T Krátký… - Engineering …, 2024 - Taylor & Francis
The hydraulic design of a pump is a challenging optimization problem. It has multiple
conflicting objective functions based on computationally very expensive (16–20 hours) …
conflicting objective functions based on computationally very expensive (16–20 hours) …
A surrogate-assisted a priori multiobjective evolutionary algorithm for constrained multiobjective optimization problems
J Hakanen, K Miettinen - Journal of Global Optimization, 2024 - Springer
We consider multiobjective optimization problems with at least one computationally
expensive constraint function and propose a novel surrogate-assisted evolutionary …
expensive constraint function and propose a novel surrogate-assisted evolutionary …
Transferable preference learning in multi-objective decision analysis and its application to hydrocracking
Hydrocracking represents a complex and time-consuming chemical process that converts
heavy oil fractions into various valuable products with low boiling points. It plays a pivotal …
heavy oil fractions into various valuable products with low boiling points. It plays a pivotal …
Surrogate-assisted parameter re-initialization for differential evolution
With respect to parameter adaptation in meta-heuristic algorithms, a kind of feedback usually
exists between parameter adaptation and individuals' survival. It is known that parameters in …
exists between parameter adaptation and individuals' survival. It is known that parameters in …
The multi-objective optimisation of breakwaters using evolutionary approach
NO Nikitin, IS Polonskaia… - … and Engineering 5 …, 2021 - taylorfrancis.com
In engineering practice, it is often necessary to increase the effectiveness of existing
protective constructions for ports and coasts (ie breakwaters) by extending their …
protective constructions for ports and coasts (ie breakwaters) by extending their …
Preference-based Evolutionary Multiobjective Optimization: Methods, Performance Indicators, and Applications
P Aghaei Pour - JYU dissertations, 2022 - jyx.jyu.fi
Monitavoiteoptimointiongelmissa optimoidaan useita ristiriitaisia tavoitefunktioita
samanaikaisesti. Ristiriitaisuuden vuoksi niillä on useita ns. Pareto-optimaalisia ratkaisuja …
samanaikaisesti. Ristiriitaisuuden vuoksi niillä on useita ns. Pareto-optimaalisia ratkaisuja …