Multi-and many-objective optimization: present and future in de novo drug design
de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting
objectives. Since several desired properties can be considered in the optimization process …
objectives. Since several desired properties can be considered in the optimization process …
An improved two-archive artificial bee colony algorithm for many-objective optimization
Artificial bee colony (ABC) algorithm has shown good performance on many optimization
problems. However, these problems mainly focus on single-objective and ordinary multi …
problems. However, these problems mainly focus on single-objective and ordinary multi …
A dual distance dominance based evolutionary algorithm with selection-replacement operator for many-objective optimization
W Zhang, J Liu, J Liu, Y Liu, S Tan - Expert Systems with Applications, 2024 - Elsevier
Most existing dominance relations give higher priority to convergence than diversity and
cannot offer reasonable selection pressure according to the evolution status. This easily …
cannot offer reasonable selection pressure according to the evolution status. This easily …
An indicator preselection based evolutionary algorithm with auxiliary angle selection for many-objective optimization
Q Gu, Q Zhou, Q Wang, NN Xiong - Information Sciences, 2023 - Elsevier
Many-objective evolutionary algorithms (MaOEAs) have received significant achievements
in recent years. Maintaining a balance between convergence and diversity becomes a key …
in recent years. Maintaining a balance between convergence and diversity becomes a key …
Multi-objective decomposition evolutionary algorithm with objective modification-based dominance and external archive
In practice, the multi-objective optimization problem (MOP) is typically challenging in two
aspects. On the one hand, its Pareto front has imbalanced search difficulties; on the other …
aspects. On the one hand, its Pareto front has imbalanced search difficulties; on the other …
ACDB-EA: Adaptive convergence-diversity balanced evolutionary algorithm for many-objective optimization
Recently, evolutionary algorithms (EAs) have shown their strong competitiveness in
handling many-objective optimization problems (MaOPs) with different Pareto fronts (PFs) …
handling many-objective optimization problems (MaOPs) with different Pareto fronts (PFs) …
Multiobjective optimization for improving throughput and energy efficiency in UAV-enabled IoT
Unmanned-aerial-vehicle (UAV)-aided wireless communication in Internet of Things (IoT)
applications is becoming the focus of attention of researchers. This article investigates a …
applications is becoming the focus of attention of researchers. This article investigates a …
A many-objective optimization evolutionary algorithm based on hyper-dominance degree
Z Liu, F Han, Q Ling, H Han, J Jiang - Swarm and Evolutionary …, 2023 - Elsevier
Compared with multi-objective optimization, solving many-objective optimization problems
usually require more strong selection pressure. However, too strong selection pressure …
usually require more strong selection pressure. However, too strong selection pressure …
A distance and cosine similarity-based fitness evaluation mechanism for large-scale many-objective optimization
The fitness evaluation mechanism (FEM) based on nondominated sorting may lead to slow
convergence when solving large-scale many-objective optimization problems (LSMaOPs) …
convergence when solving large-scale many-objective optimization problems (LSMaOPs) …
MaOEA/D with adaptive external population guided weight vector adjustment
Y Sun, J Liu, Z Liu - Expert Systems with Applications, 2024 - Elsevier
In order to make multiobjective evolutionary algorithm based on decomposition (MOEA/D)
have better performance in dealing with many-objective optimization problems (MaOPs) with …
have better performance in dealing with many-objective optimization problems (MaOPs) with …