Real-time data-driven automatic design of multi-objective evolutionary algorithm: A case study on production scheduling
Multi-objective evolutionary algorithms (MOEAs) have become an important choice for
solving multi-objective optimization problems. The performance of MOEAs is highly …
solving multi-objective optimization problems. The performance of MOEAs is highly …
An ACO-based hyper-heuristic for sequencing many-objective evolutionary algorithms that consider different ways to incorporate the DM's preferences
Many-objective optimization is an area of interest common to researchers, professionals,
and practitioners because of its real-world implications. Preference incorporation into Multi …
and practitioners because of its real-world implications. Preference incorporation into Multi …
Hybridisation of swarm intelligence algorithms with multi-criteria ordinal classification: A strategy to address many-objective optimisation
This paper introduces a strategy to enrich swarm intelligence algorithms with the
preferences of the Decision Maker (DM) represented in an ordinal classifier based on …
preferences of the Decision Maker (DM) represented in an ordinal classifier based on …
A Kriging-assisted evolutionary algorithm with multiple infill sampling for expensive many-objective optimization
Surrogate-assisted evolutionary algorithms (SAEAs) have been extensively used to solve
computationally expensive multi-objective optimization problems (MOPs) as they can obtain …
computationally expensive multi-objective optimization problems (MOPs) as they can obtain …
A novel Bayesian approach for multi-objective stochastic simulation optimization
M Han, L Ouyang - Swarm and Evolutionary Computation, 2022 - Elsevier
Multi-objective stochastic simulation optimization plays an important role in designing
complex engineering systems. To identify optimal solutions via simulations, Bayesian …
complex engineering systems. To identify optimal solutions via simulations, Bayesian …
[HTML][HTML] ESG integration in portfolio selection: A robust preference-based multicriteria approach
We present a framework for multi-objective optimization where the classical mean–variance
portfolio model is extended to integrate the environmental, social and governance (ESG) …
portfolio model is extended to integrate the environmental, social and governance (ESG) …
[HTML][HTML] MOSA/DO and MOSAD/DO-II: Performance analysis of decomposition-based algorithms in many objective problems
In recent years, many-objective optimization problems (MaOPs) have been challenging.
Classically, algorithms obtain first the Pareto front (PF). Next, a decision maker (DM) can …
Classically, algorithms obtain first the Pareto front (PF). Next, a decision maker (DM) can …
User-Preference Based Evolutionary Algorithms for Solving Multi-Objective Nonlinear Minimum Cost Flow Problems
Network flow optimisation has various applications such as communication, transportation,
computer networks and logistics. The minimum cost flow problem (MCFP) is the most …
computer networks and logistics. The minimum cost flow problem (MCFP) is the most …
An interactive ACO enriched with an eclectic multi-criteria ordinal classifier to address many-objective optimisation problems
Despite the vast research on many-objective optimisation problems, the presence of many
objective functions is still a challenge worthy of further study. A way to treat this kind of …
objective functions is still a challenge worthy of further study. A way to treat this kind of …
Merging preferences into the best solution seeking for many-objective optimization problems
J Yang, X **a, XL Wang, Q Jiang, K **ng - Expert Systems with Applications, 2024 - Elsevier
Finding out a best solution of the multi-objective optimization problem (MOP) or many-
objective optimization problem (MaOP) in the user's view, is a significant and practical …
objective optimization problem (MaOP) in the user's view, is a significant and practical …