Evolutionary large-scale multi-objective optimization: A survey

Y Tian, L Si, X Zhang, R Cheng, C He… - ACM Computing …, 2021 - dl.acm.org
Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …

A benchmark-suite of real-world constrained multi-objective optimization problems and some baseline results

A Kumar, G Wu, MZ Ali, Q Luo, R Mallipeddi… - Swarm and Evolutionary …, 2021 - Elsevier
Abstract Generally, Synthetic Benchmark Problems (SBPs) are utilized to assess the
performance of metaheuristics. However, these SBPs may include various unrealistic …

An evolutionary algorithm for large-scale sparse multiobjective optimization problems

Y Tian, X Zhang, C Wang, Y Jin - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
In the last two decades, a variety of different types of multiobjective optimization problems
(MOPs) have been extensively investigated in the evolutionary computation community …

An easy-to-use real-world multi-objective optimization problem suite

R Tanabe, H Ishibuchi - Applied Soft Computing, 2020 - Elsevier
Although synthetic test problems are widely used for the performance assessment of
evolutionary multi-objective optimization algorithms, they are likely to include unrealistic …

A survey on learnable evolutionary algorithms for scalable multiobjective optimization

S Liu, Q Lin, J Li, KC Tan - IEEE Transactions on Evolutionary …, 2023 - ieeexplore.ieee.org
Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …

A scalable indicator-based evolutionary algorithm for large-scale multiobjective optimization

W Hong, K Tang, A Zhou, H Ishibuchi… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
The performance of traditional multiobjective evolutionary algorithms (MOEAs) often
deteriorates rapidly as the number of decision variables increases. While some efforts were …

Review of the research landscape of multi-criteria evaluation and benchmarking processes for many-objective optimization methods: coherent taxonomy, challenges …

RT Mohammed, R Yaakob, AA Zaidan… - … Journal of Information …, 2020 - World Scientific
Evaluation and benchmarking of many-objective optimization (MaOO) methods are
complicated. The rapid development of new optimization algorithms for solving problems …

A survey of normalization methods in multiobjective evolutionary algorithms

L He, H Ishibuchi, A Trivedi, H Wang… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
A real-world multiobjective optimization problem (MOP) usually has differently scaled
objectives. Objective space normalization has been widely used in multiobjective …

Evolutionary large-scale multiobjective optimization: Benchmarks and algorithms

S Liu, Q Lin, KC Wong, Q Li… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Evolutionary large-scale multiobjective optimization (ELMO) has received increasing
attention in recent years. This study has compared various existing optimizers for ELMO on …

A new many-objective evolutionary algorithm based on determinantal point processes

P Zhang, J Li, T Li, H Chen - IEEE Transactions on Evolutionary …, 2020 - ieeexplore.ieee.org
To handle different types of many-objective optimization problems (MaOPs), many-objective
evolutionary algorithms (MaOEAs) need to simultaneously maintain convergence and …