Evolutionary large-scale multi-objective optimization: A survey
Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …
solving various optimization problems, but their performance may deteriorate drastically …
A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems
In the last few years, the formulation of real-world optimization problems and their efficient
solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In …
solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In …
A survey on evolutionary constrained multiobjective optimization
Handling constrained multiobjective optimization problems (CMOPs) is extremely
challenging, since multiple conflicting objectives subject to various constraints require to be …
challenging, since multiple conflicting objectives subject to various constraints require to be …
Multi-strategy competitive-cooperative co-evolutionary algorithm and its application
X Zhou, X Cai, H Zhang, Z Zhang, T **, H Chen… - Information …, 2023 - Elsevier
In order to effectively solve multi-objective optimization problems (MOPs) and fully balance
uniformity and convergence, a multi-strategy competitive-cooperative co-evolutionary …
uniformity and convergence, a multi-strategy competitive-cooperative co-evolutionary …
Pymoo: Multi-objective optimization in python
Python has become the programming language of choice for research and industry projects
related to data science, machine learning, and deep learning. Since optimization is an …
related to data science, machine learning, and deep learning. Since optimization is an …
A coevolutionary framework for constrained multiobjective optimization problems
Constrained multiobjective optimization problems (CMOPs) are challenging because of the
difficulty in handling both multiple objectives and constraints. While some evolutionary …
difficulty in handling both multiple objectives and constraints. While some evolutionary …
An evolutionary multitasking optimization framework for constrained multiobjective optimization problems
When addressing constrained multiobjective optimization problems (CMOPs) via
evolutionary algorithms, various constraints and multiple objectives need to be satisfied and …
evolutionary algorithms, various constraints and multiple objectives need to be satisfied and …
Utilizing the relationship between unconstrained and constrained Pareto fronts for constrained multiobjective optimization
Constrained multiobjective optimization problems (CMOPs) involve multiple objectives to be
optimized and various constraints to be satisfied, which challenges the evolutionary …
optimized and various constraints to be satisfied, which challenges the evolutionary …
A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classification
Feature selection (FS) is an important data pre-processing technique in classification. In
most cases, FS can improve classification accuracy and reduce feature dimension, so it can …
most cases, FS can improve classification accuracy and reduce feature dimension, so it can …
A benchmark-suite of real-world constrained multi-objective optimization problems and some baseline results
Abstract Generally, Synthetic Benchmark Problems (SBPs) are utilized to assess the
performance of metaheuristics. However, these SBPs may include various unrealistic …
performance of metaheuristics. However, these SBPs may include various unrealistic …