An adaptive evolutionary algorithm based on non-euclidean geometry for many-objective optimization
A Panichella - Proceedings of the genetic and evolutionary …, 2019 - dl.acm.org
In the last decade, several evolutionary algorithms have been proposed in the literature for
solving multi-and many-objective optimization problems. The performance of such …
solving multi-and many-objective optimization problems. The performance of such …
Local model-based Pareto front estimation for multiobjective optimization
The Pareto front (PF) estimation has become an emerging strategy for solving multiobjective
optimization problems in recent studies. By approximating the geometrical structure of the …
optimization problems in recent studies. By approximating the geometrical structure of the …
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 …
A new hypervolume-based evolutionary algorithm for many-objective optimization
In this article, a new hypervolume-based evolutionary multiobjective optimization algorithm
(EMOA), namely, R2HCA-EMOA (R2-based hypervolume contribution approximation …
(EMOA), namely, R2HCA-EMOA (R2-based hypervolume contribution approximation …
Interval multiobjective optimization with memetic algorithms
J Sun, Z Miao, D Gong, XJ Zeng, J Li… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
One of the most important and widely faced optimization problems in real applications is the
interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary …
interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary …
An improved Pareto front modeling algorithm for large-scale many-objective optimization
A Panichella - Proceedings of the Genetic and Evolutionary …, 2022 - dl.acm.org
A key idea in many-objective optimization is to approximate the optimal Pareto front using a
set of representative non-dominated solutions. The produced solution set should be close to …
set of representative non-dominated solutions. The produced solution set should be close to …
A multistage evolutionary algorithm for better diversity preservation in multiobjective optimization
Diversity preservation is a crucial technique in multiobjective evolutionary algorithms
(MOEAs), which aims at evolving the population toward the Pareto front (PF) with a uniform …
(MOEAs), which aims at evolving the population toward the Pareto front (PF) with a uniform …
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 …
Constrained multimodal multi-objective optimization: Test problem construction and algorithm design
Solving multimodal multi-objective optimization problems (MMOPs) has received increasing
attention. However, recent studies only consider unconstrained MMOPs. Given the fact that …
attention. However, recent studies only consider unconstrained MMOPs. Given the fact that …
Model-based evolutionary algorithms: a short survey
The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for
solving complex optimization problems. Since the operators (eg crossover, mutation …
solving complex optimization problems. Since the operators (eg crossover, mutation …