Many-objective evolutionary algorithms: A survey
Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world
applications. However, most MOEAs based on Pareto-dominance handle many-objective …
applications. However, most MOEAs based on Pareto-dominance handle many-objective …
Balancing convergence and diversity in decomposition-based many-objective optimizers
The decomposition-based multiobjective evolutionary algorithms (MOEAs) generally make
use of aggregation functions to decompose a multiobjective optimization problem into …
use of aggregation functions to decompose a multiobjective optimization problem into …
Fuzzy-based Pareto optimality for many-objective evolutionary algorithms
Evolutionary algorithms have been effectively used to solve multiobjective optimization
problems with a small number of objectives, two or three in general. However, when …
problems with a small number of objectives, two or three in general. However, when …
A review of multi-objective optimisation and decision making using evolutionary algorithms
Research in the field of multi-objective optimisation problem (MOP) has garnered ample
interest in the last two decades. Majority of methods developed for solving the problem …
interest in the last two decades. Majority of methods developed for solving the problem …
A decomposition-based many-objective evolutionary algorithm with two types of adjustments for direction vectors
X Cai, Z Mei, Z Fan - IEEE Transactions on Cybernetics, 2017 - ieeexplore.ieee.org
Decomposition-based multiobjective evolutionary algorithm has shown its advantage in
addressing many-objective optimization problem (MaOP). To further improve its …
addressing many-objective optimization problem (MaOP). To further improve its …
Multiobjective estimation of distribution algorithm based on joint modeling of objectives and variables
This paper proposes a new multiobjective estimation of distribution algorithm (EDA) based
on joint probabilistic modeling of objectives and variables. This EDA uses the …
on joint probabilistic modeling of objectives and variables. This EDA uses the …
A survey of decomposition methods for multi-objective optimization
The multi-objective optimization methods are traditionally based on Pareto dominance or
relaxed forms of dominance in order to achieve a representation of the Pareto front …
relaxed forms of dominance in order to achieve a representation of the Pareto front …
Analysis of inverted PBI and comparison with other scalarizing functions in decomposition based MOEAs
H Sato - Journal of Heuristics, 2015 - Springer
MOEA/D is one of the promising evolutionary approaches for solving multi and many-
objective optimization problems. MOEA/D decomposes a multi-objective optimization …
objective optimization problems. MOEA/D decomposes a multi-objective optimization …
Many-objective artificial bee colony algorithm for large-scale software module clustering problem
The meta-heuristic search algorithms have been widely applied to solve the various science
and engineering optimization problems. However, the performance of these algorithms is …
and engineering optimization problems. However, the performance of these algorithms is …
Many-objective evolutionary algorithm based on relative non-dominance matrix
Various evolutionary algorithms have been proposed for tackling many-objective
optimization problems over the past three decades. However, these algorithms still suffer …
optimization problems over the past three decades. However, these algorithms still suffer …