Results 231 to 240 of about 609,298 (293)

Evolutionary Large-Scale Multi-Objective Optimization: A Survey

ACM Computing Surveys, 2021
Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective
Tian, Ye   +6 more
openaire   +2 more sources

Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization

Neural Computing and Applications, 2020
Many engineering optimization problems are typically multi-objective in their natures and multidisciplinary with a large number of decision variables. Furthermore, Pareto dominance loses its effectiveness in such situations. Thus, developing a robust optimization algorithm undoubtedly becomes a true challenge.
Rizk M. Rizk-Allah   +2 more
openaire   +2 more sources

Coevolutionary Operations for Large Scale Multi-objective Optimization

2020 IEEE Congress on Evolutionary Computation (CEC), 2020
Multi-objective evolutionary algorithms (MOEAs) of the state of the art are created with the only purpose of dealing with the number of objective functions in a multi-objective optimization problem (MOP) and treat the decision variables of a MOP as a whole.
Luis Miguel Antonio   +5 more
openaire   +2 more sources

Operational decomposition for large scale multi-objective optimization problems

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019
Most multi-objective evolutionary algorithms (MOEAs) of the state of the art treat the decision variables of a multi-objective optimization problem (MOP) as a whole. However, when dealing with MOPs with a large number of decision variables (more than 100) their efficacy decreases as the number of decision variables of the MOP increases.
Luis Miguel Antonio   +4 more
openaire   +2 more sources

Weighted Optimization Framework for Large-scale Multi-objective Optimization

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, 2016
In this work we introduce a new method for solving multi-objective optimization problems that involve a large number of decision variables. The proposed Weighted Optimization Framework (WOF) relies on variable grouping and weighting to transform the original optimization problem and is designed as a generic method that can be used with any population ...
Heiner Zille   +3 more
openaire   +2 more sources

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