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 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 …
An evolutionary algorithm for large-scale sparse multiobjective optimization problems
In the last two decades, a variety of different types of multiobjective optimization problems
(MOPs) have been extensively investigated in the evolutionary computation community …
(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 …
evolutionary multi-objective optimization algorithms, they are likely to include unrealistic …
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 …
A scalable indicator-based evolutionary algorithm for large-scale multiobjective optimization
The performance of traditional multiobjective evolutionary algorithms (MOEAs) often
deteriorates rapidly as the number of decision variables increases. While some efforts were …
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 …
complicated. The rapid development of new optimization algorithms for solving problems …
A survey of normalization methods in multiobjective evolutionary algorithms
A real-world multiobjective optimization problem (MOP) usually has differently scaled
objectives. Objective space normalization has been widely used in multiobjective …
objectives. Objective space normalization has been widely used in multiobjective …
Evolutionary large-scale multiobjective optimization: Benchmarks and algorithms
Evolutionary large-scale multiobjective optimization (ELMO) has received increasing
attention in recent years. This study has compared various existing optimizers for ELMO on …
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
To handle different types of many-objective optimization problems (MaOPs), many-objective
evolutionary algorithms (MaOEAs) need to simultaneously maintain convergence and …
evolutionary algorithms (MaOEAs) need to simultaneously maintain convergence and …