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GECCO
2007
Springer
176views Optimization» more  GECCO 2007»
13 years 11 months ago
Two-level of nondominated solutions approach to multiobjective particle swarm optimization
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...
M. A. Abido
TEC
2002
161views more  TEC 2002»
13 years 4 months ago
A fast and elitist multiobjective genetic algorithm: NSGA-II
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...
GECCO
2007
Springer
300views Optimization» more  GECCO 2007»
13 years 11 months ago
Methodology to select solutions from the pareto-optimal set: a comparative study
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...
José C. Ferreira, Carlos M. Fonseca, Ant&oa...
GECCO
2008
Springer
148views Optimization» more  GECCO 2008»
13 years 5 months ago
Accelerating convergence using rough sets theory for multi-objective optimization problems
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 ...
Luis V. Santana-Quintero, Carlos A. Coello Coello
CEC
2009
IEEE
13 years 11 months ago
A clustering multi-objective evolutionary algorithm based on orthogonal and uniform design
Abstract— Designing efficient algorithms for difficult multiobjective optimization problems is a very challenging problem. In this paper a new clustering multi-objective evolut...
Yuping Wang, Chuangyin Dang, Hecheng Li, Lixia Han...