A comprehensive survey on NSGA-II for multi-objective optimization and applications
H Ma, Y Zhang, S Sun, T Liu, Y Shan - Artificial Intelligence Review, 2023 - Springer
In the last two decades, the fast and elitist non-dominated sorting genetic algorithm (NSGA-
II) has attracted extensive research interests, and it is still one of the hottest research …
II) has attracted extensive research interests, and it is still one of the hottest research …
A new local search-based multiobjective optimization algorithm
In this paper, a new multiobjective optimization framework based on nondominated sorting
and local search (NSLS) is introduced. The NSLS is based on iterations. At each iteration …
and local search (NSLS) is introduced. The NSLS is based on iterations. At each iteration …
An improved adaptive approach for elitist nondominated sorting genetic algorithm for many-objective optimization
NSGA-II and its contemporary EMO algorithms were found to be vulnerable in solving many-
objective optimization problems having four or more objectives. It is not surprising that EMO …
objective optimization problems having four or more objectives. It is not surprising that EMO …
Improved NSGA-III with selection-and-elimination operator
Z Cui, Y Chang, J Zhang, X Cai, W Zhang - Swarm and Evolutionary …, 2019 - Elsevier
A fast non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III)
is a well-known many-objective optimization algorithm in which the reference-point strategy …
is a well-known many-objective optimization algorithm in which the reference-point strategy …
An improved adaptive NSGA-II with multi-population algorithm
Z Zhao, B Liu, C Zhang, H Liu - Applied Intelligence, 2019 - Springer
The NSGA-II algorithm uses a single population single crossover operator, which limits the
search performance of the algorithm to a certain extent. This paper presents an improved …
search performance of the algorithm to a certain extent. This paper presents an improved …
Effectiveness and efficiency of non-dominated sorting for evolutionary multi-and many-objective optimization
Since non-dominated sorting was first adopted in NSGA in 1995, most evolutionary
algorithms have employed non-dominated sorting as one of the major criteria in their …
algorithms have employed non-dominated sorting as one of the major criteria in their …
A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems
This paper provides an extensive review of the popular multi-objective optimization
algorithm NSGA-II for selected combinatorial optimization problems viz. assignment …
algorithm NSGA-II for selected combinatorial optimization problems viz. assignment …
U-NSGA-III: a unified evolutionary optimization procedure for single, multiple, and many objectives: proof-of-principle results
Evolutionary algorithms (EAs) have been systematically developed to solve mono-objective,
multi-objective and many-objective problems, respectively, over the past few decades …
multi-objective and many-objective problems, respectively, over the past few decades …
NSGA-II with simple modification works well on a wide variety of many-objective problems
In the last two decades, the non-dominated sorting genetic algorithm II (NSGA-II) has been
the most widely-used evolutionary multi-objective optimization (EMO) algorithm. However …
the most widely-used evolutionary multi-objective optimization (EMO) algorithm. However …
A new decomposition-based NSGA-II for many-objective optimization
Multiobjective evolutionary algorithms (MOEAs) have proven their effectiveness and
efficiency in solving problems with two or three objectives. However, recent studies show …
efficiency in solving problems with two or three objectives. However, recent studies show …