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A fast and elitist multiobjective genetic algorithm: NSGA-II (2002)
| Content Provider | CiteSeerX |
|---|---|
| Author | Deb, Kalyanmoy Pratap, Amrit Agarwal, Sameer Meyarivan, T. |
| Abstract | Abstract—Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criti-cized mainly for their: 1) ( 3) computational complexity (where is the number of objectives and is the population size); 2) nonelitism approach; and 3) the need for specifying a sharing parameter. In this paper, we suggest a nondominated sorting-based multiobjective EA (MOEA), called nondominated sorting genetic algorithm II (NSGA-II), which alleviates all the above three difficulties. Specifically, a fast nondominated sorting approach with ( 2) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best (with respect to fitness and spread) solutions. Simulation results on difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to Pareto-archived evolution strategy and strength-Pareto EA—two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multiobjective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective seven-constraint nonlinear problem, are compared with another constrained multiobjective optimizer and much better performance of NSGA-II is observed. Index Terms—Constraint handling, elitism, genetic algorithms, multicriterion decision making, multiobjective optimization, Pareto-optimal solutions. I. |
| File Format | |
| Journal | IEEE Trans. Evolutionary Computation |
| Language | English |
| Publisher Date | 2002-01-01 |
| Access Restriction | Open |
| Subject Keyword | Elitist Multiobjective Genetic Algorithm Computational Complexity Simulation Result Mating Pool Difficult Test Problem Special Attention Strength-pareto Ea Pareto-archived Evolution Strategy Multiobjective Optimization Pareto-optimal Solution Genetic Algorithm Ii Diverse Pareto-optimal Front Sharing Parameter Index Term Constraint Handling Test Problem Constrained Multiobjective Problem Nonelitism Approach Elitist Moeas Abstract Multiobjective Evolutionary Algorithm Nondominated Sorting-based Multiobjective Ea Genetic Algorithm Multicriterion Decision Making Population Size Five-objective Seven-constraint Nonlinear Problem Selection Operator Multiobjective Optimizer True Pareto-optimal Front |
| Content Type | Text |
| Resource Type | Article |
