The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making
Evolutionary multiobjective optimization (EMO) methodologies have gained popularity in
finding a representative set of Pareto optimal solutions in the past decade and beyond …
finding a representative set of Pareto optimal solutions in the past decade and beyond …
A decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penalty
A multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a
multiobjective optimization problem (MOP) into a number of scalar optimization subproblems …
multiobjective optimization problem (MOP) into a number of scalar optimization subproblems …
An adaptive evolutionary multi-objective approach based on simulated annealing
H Li, D Landa-Silva - Evolutionary computation, 2011 - direct.mit.edu
A multi-objective optimization problem can be solved by decomposing it into one or more
single objective subproblems in some multi-objective metaheuristic algorithms. Each …
single objective subproblems in some multi-objective metaheuristic algorithms. Each …
DMOEA- : Decomposition-Based Multiobjective Evolutionary Algorithm With the -Constraint Framework
Decomposition is an efficient and prevailing strategy for solving multiobjective optimization
problems (MOPs). Its success has been witnessed by the multiobjective evolutionary …
problems (MOPs). Its success has been witnessed by the multiobjective evolutionary …
Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has shown a
superior performance in tackling some complicated multiobjective optimization problems …
superior performance in tackling some complicated multiobjective optimization problems …
An interactive evolutionary multi-objective optimization and decision making procedure
S Chaudhuri, K Deb - Applied Soft Computing, 2010 - Elsevier
With the advent of efficient techniques for multi-objective evolutionary optimization (EMO),
real-world search and optimization problems are being increasingly solved for multiple …
real-world search and optimization problems are being increasingly solved for multiple …
A preference-based evolutionary algorithm for multi-objective optimization
L Thiele, K Miettinen, PJ Korhonen… - Evolutionary …, 2009 - ieeexplore.ieee.org
In this paper, we discuss the idea of incorporating preference information into evolutionary
multi-objective optimization and propose a preference-based evolutionary approach that …
multi-objective optimization and propose a preference-based evolutionary approach that …
A direct local search mechanism for decomposition-based multi-objective evolutionary algorithms
SZ Martínez, CAC Coello - 2012 IEEE congress on evolutionary …, 2012 - ieeexplore.ieee.org
In recent years, the development of multi-objective evolutionary algorithms (MOEAs)
hybridized with mathematical programming techniques has significantly increased …
hybridized with mathematical programming techniques has significantly increased …
Multi-objective evolutionary algorithms: Introducing bias among Pareto-optimal solutions
K Deb - Advances in evolutionary computing: theory and …, 2003 - Springer
Since the beginning of the 1990s, research and application of multiobjective evolutionary
algorithms (MOEAs) have attracted increasing attention. This is mainly due to the ability of …
algorithms (MOEAs) have attracted increasing attention. This is mainly due to the ability of …
A new decomposition-based NSGA-II for many-objective optimization
Multiobjective evolutionary algorithms (MOEAs) have proven their effectiveness and
efficiency in solving problems with two or three objectives. However, recent studies show …
efficiency in solving problems with two or three objectives. However, recent studies show …