In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle of the population has a great impact on the convergen...
Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) ( 3) computational complexity (where is the number ...
Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, T. Mey...
The resolution of a Multi-Objective Optimization Problem (MOOP) does not end when the Pareto-optimal set is found. In real problems, a single solution must be selected. Ideally, t...
We propose the use of rough sets theory to improve the first approximation provided by a multi-objective evolutionary algorithm and retain the nondominated solutions using a new ...
Abstract— Designing efficient algorithms for difficult multiobjective optimization problems is a very challenging problem. In this paper a new clustering multi-objective evolut...