Learning to accelerate evolutionary search for large-scale multiobjective optimization
Most existing evolutionary search strategies are not so efficient when directly handling the
decision space of large-scale multiobjective optimization problems (LMOPs). To enhance …
decision space of large-scale multiobjective optimization problems (LMOPs). To enhance …
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 …
Enhanced Innovized Progress Operator for Evolutionary Multi- and Many-Objective Optimization
Innovization is a task of learning common relationships among some or all of the Pareto-
optimal (PO) solutions in multi-and many-objective optimization problems. A recent study …
optimal (PO) solutions in multi-and many-objective optimization problems. A recent study …
Machine learning-based framework to cover optimal Pareto-front in many-objective optimization
One of the crucial challenges of solving many-objective optimization problems is uniformly
well covering of the Pareto-front (PF). However, many the state-of-the-art optimization …
well covering of the Pareto-front (PF). However, many the state-of-the-art optimization …
A Learning-based Innovized Progress Operator for Faster Convergence in Evolutionary Multi-objective Optimization
Learning effective problem information from already explored search space in an
optimization run, and utilizing it to improve the convergence of subsequent solutions, have …
optimization run, and utilizing it to improve the convergence of subsequent solutions, have …
Growing Neural Gas Network for Offspring Generation in Evolutionary Constrained Multi-Objective Optimization
C Wang, H Huang, X Zhang - IEEE Transactions on Emerging …, 2023 - ieeexplore.ieee.org
When using evolutionary algorithms to solve constrained multi-objective optimization
problems, both the constraint handling technique and the search operator play a crucial role …
problems, both the constraint handling technique and the search operator play a crucial role …
Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions
H Sato, H Ishibuchi - IEEJ Transactions on Electrical and …, 2023 - Wiley Online Library
Population‐based evolutionary algorithms are suitable for solving multi‐objective
optimization problems involving multiple conflicting objectives. This is because a set of well …
optimization problems involving multiple conflicting objectives. This is because a set of well …
A population hierarchical-based evolutionary algorithm for large-scale many-objective optimization
In large-scale many-objective optimization problems (LMaOPs), the performance of
algorithms faces significant challenges as the number of objective functions and decision …
algorithms faces significant challenges as the number of objective functions and decision …
Compromising Pareto-Optimality With Regularity in Platform-Based Multi-Objective Optimization
Multi-objective optimization problems give rise to a set of Pareto-optimal solutions, each of
which makes a trade-off among the objectives. When multiple Pareto-optimal solutions are …
which makes a trade-off among the objectives. When multiple Pareto-optimal solutions are …
A Grey Prediction‐Based Reproduction Strategy for Many‐Objective Evolutionary Algorithm
LS Wei, EC Li - International Journal of Intelligent Systems, 2024 - Wiley Online Library
Many‐objective evolutionary algorithms (MaOEAs) consisted of environmental selection and
reproduction operator. However, few studies focus on how to design reproduction operators …
reproduction operator. However, few studies focus on how to design reproduction operators …