A survey of evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts
Evolutionary algorithms have been shown to be very successful in solving multi-objective
optimization problems (MOPs). However, their performance often deteriorates when solving …
optimization problems (MOPs). However, their performance often deteriorates when solving …
A reference vector guided evolutionary algorithm for many-objective optimization
In evolutionary multiobjective optimization, maintaining a good balance between
convergence and diversity is particularly crucial to the performance of the evolutionary …
convergence and diversity is particularly crucial to the performance of the evolutionary …
A survey of multiobjective evolutionary algorithms based on decomposition
Decomposition is a well-known strategy in traditional multiobjective optimization. However,
the decomposition strategy was not widely employed in evolutionary multiobjective …
the decomposition strategy was not widely employed in evolutionary multiobjective …
Multi-strategy multi-objective differential evolutionary algorithm with reinforcement learning
Multiobjective evolutionary algorithms (MOEAs) have gained much attention due to their
high effectiveness and efficiency in solving multiobjective optimization problems (MOPs) …
high effectiveness and efficiency in solving multiobjective optimization problems (MOPs) …
Decomposition-based algorithms using Pareto adaptive scalarizing methods
Decomposition-based algorithms have become increasingly popular for evolutionary
multiobjective optimization. However, the effect of scalarizing methods used in these …
multiobjective optimization. However, the effect of scalarizing methods used in these …
A review of multi-objective optimisation and decision making using evolutionary algorithms
Research in the field of multi-objective optimisation problem (MOP) has garnered ample
interest in the last two decades. Majority of methods developed for solving the problem …
interest in the last two decades. Majority of methods developed for solving the problem …
Methods for multi-objective optimization: An analysis
I Giagkiozis, PJ Fleming - Information Sciences, 2015 - Elsevier
Decomposition-based methods are often cited as the solution to multi-objective nonconvex
optimization problems with an increased number of objectives. These methods employ a …
optimization problems with an increased number of objectives. These methods employ a …
What weights work for you? Adapting weights for any Pareto front shape in decomposition-based evolutionary multiobjective optimisation
M Li, X Yao - Evolutionary Computation, 2020 - direct.mit.edu
The quality of solution sets generated by decomposition-based evolutionary multi-objective
optimisation (EMO) algorithms depends heavily on the consistency between a given …
optimisation (EMO) algorithms depends heavily on the consistency between a given …
Multiple reference points-based decomposition for multiobjective feature selection in classification: Static and dynamic mechanisms
Feature selection is an important task in machine learning that has two main objectives: 1)
reducing dimensionality and 2) improving learning performance. Feature selection can be …
reducing dimensionality and 2) improving learning performance. Feature selection can be …
Constrained subproblems in a decomposition-based multiobjective evolutionary algorithm
A decomposition approach decomposes a multiobjective optimization problem into a
number of scalar objective optimization subproblems. It plays a key role in decomposition …
number of scalar objective optimization subproblems. It plays a key role in decomposition …