Now showing items 11-15 of 15
Improving the Multiobjective Evolutionary Algorithm Based on Decomposition with New Penalty Schemes
It has been increasingly reported that the multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) is promising for handling multiobjective optimization problems (MOPs). MOEA/D employs scalarizing ...
An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has shown to be very efficient in solving multi-objective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs ...
Approximating Multiobjective Optimization Problems with Complex Pareto Fronts
(IEEE Press, 2015-09)
The main goal of multiobjective optimization is to achieve a set of well-converged and evenly-distributed Pareto optimal points. While evolutionary algorithms have been reported to converge well, their distribution performance ...
On the use of hypervolume for diversity measurement of Pareto front approximations
In multiobjective optimization, a good quality indicator is of great importance to the performance assessment of algorithms. This paper investigates the effectiveness of the widely-used hypervolume indicator, which is the ...
An empirical study of dynamic triobjective optimisation problems
(IEEE Press, 2018-07)
Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, ...