Review of the research landscape of multi-criteria evaluation and benchmarking processes for many-objective optimization methods: coherent taxonomy, challenges …
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 …
On the effect of reference point in MOEA/D for multi-objective optimization
Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has continuously
proven effective for multi-objective optimization. So far, the effect of weight vectors and …
proven effective for multi-objective optimization. So far, the effect of weight vectors and …
Use of two penalty values in multiobjective evolutionary algorithm based on decomposition
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) with the
penalty-based boundary intersection (PBI) function (denoted as MOEA/D-PBI) has been …
penalty-based boundary intersection (PBI) function (denoted as MOEA/D-PBI) has been …
Effects of corner weight vectors on the performance of decomposition-based multiobjective algorithms
Recently, it was demonstrated that a decomposition-based multiobjective evolutionary
algorithm with a pre-specified weight vector set cannot find a uniformly-distributed solution …
algorithm with a pre-specified weight vector set cannot find a uniformly-distributed solution …
Effect of objective normalization and penalty parameter on penalty boundary intersection decomposition-based evolutionary many-objective optimization algorithms
An objective normalization strategy is essential in any evolutionary multiobjective or many-
objective optimization (EMO or EMaO) algorithm, due to the distance calculations between …
objective optimization (EMO or EMaO) algorithm, due to the distance calculations between …
An analysis of control parameters of MOEA/D under two different optimization scenarios
An unbounded external archive (UEA), which stores all nondominated solutions found
during the search process, is frequently used to evaluate the performance of multi-objective …
during the search process, is frequently used to evaluate the performance of multi-objective …
An adaptive penalty-based boundary intersection method for many-objective optimization problem
Compared with domination-based methods, the multi-objective evolutionary algorithm
based on decomposition (MOEA/D) is less prone to the difficulty caused by an increase in …
based on decomposition (MOEA/D) is less prone to the difficulty caused by an increase in …
On the effect of localized PBI method in MOEA/D for multi-objective optimization
The idea of localization, eg, mating restriction, local search, is often employed in the design
of evolutionary multi-objective algorithms, and has been demonstrated effective in many …
of evolutionary multi-objective algorithms, and has been demonstrated effective in many …
Use of piecewise linear and nonlinear scalarizing functions in MOEA/D
A number of weight vector-based algorithms have been proposed for many-objective
optimization using the framework of MOEA/D (multi-objective evolutionary algorithm based …
optimization using the framework of MOEA/D (multi-objective evolutionary algorithm based …
On the parameter setting of the penalty-based boundary intersection method in MOEA/D
Multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D)
decomposes an multiobjective optimization problem into a number of single-objective …
decomposes an multiobjective optimization problem into a number of single-objective …