## Constrained Multi-Objective Optimization Using Steady State Genetic Algorithms (2003)

Venue: | In Proceedings of Genetic and Evolutionary Computation Conference |

Citations: | 10 - 0 self |

### BibTeX

@INPROCEEDINGS{Chafekar03constrainedmulti-objective,

author = {Deepti Chafekar and Jiang Xuan and Khaled Rasheed},

title = {Constrained Multi-Objective Optimization Using Steady State Genetic Algorithms},

booktitle = {In Proceedings of Genetic and Evolutionary Computation Conference},

year = {2003},

pages = {813--824},

publisher = {Springer-Verlag}

}

### OpenURL

### Abstract

In this paper we propose two novel approaches for solving constrained multi-objective optimization problems using steady state GAs.

### Citations

1073 |
Multi-objective optimization using evolutionary algorithms
- Deb
- 2001
(Show Context)
Citation Context ...], Pareto Envelope based selection-II (PESA-II) [17]. Most of these approaches propose the use of a generational GA. Deb proposed an Elitist Steady State Multi-objective Evolutionary Algorithm (MOEA) =-=[18]-=- which attempts to maintain spread [15] while attempting to converge to the true Pareto-optimal front. This algorithm requires sorting of the population for every new solution formed thereby increasin... |

406 | L.: SPEA2: improving the strength pareto evolutionary algorithm for multiobjective optimization, Research Report
- Zitzler, Thiele
- 2001
(Show Context)
Citation Context ...g multi-objective optimization problems have been developed. The most recent ones are the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) [3], Strength Pareto Evolutionary Algorithm-II (SPEA-II) =-=[16]-=-, Pareto Envelope based selection-II (PESA-II) [17]. Most of these approaches propose the use of a generational GA. Deb proposed an Elitist Steady State Multi-objective Evolutionary Algorithm (MOEA) [... |

403 | A fast élitist non dominated sorting genetic algorithm for multi objective optimization
- Deb, Agrawal, et al.
- 2000
(Show Context)
Citation Context ... [9]. Since then, many Evolutionary algorithms for solving multi-objective optimization problems have been developed. The most recent ones are the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) =-=[3]-=-, Strength Pareto Evolutionary Algorithm-II (SPEA-II) [16], Pareto Envelope based selection-II (PESA-II) [17]. Most of these approaches propose the use of a generational GA. Deb proposed an Elitist St... |

90 |
K.: Multi-objective function optimization using nondominated sorting genetic algorithms
- Srinivas, Deb
- 1994
(Show Context)
Citation Context ...omains. The degree of difficulty of these problems varies from fairly simple to difficult. The problems chosen from the benchmark domains are BNH used by Binh and Korn [10], SRN used by Srinivas, Deb =-=[11]-=-, TNK suggested by Tanaka [12] and OSY used by Osyczka, Kundu [13]. The problems chosen from the engineering domains are Two-Bar Truss Design used by Deb [14] and Welded Beam design used by Deb [14]. ... |

57 | The Pareto Envelope-based Selection Algorithm for Multi-objective Optimization
- Crone, Knowles, et al.
- 2000
(Show Context)
Citation Context ...eveloped. The most recent ones are the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) [3], Strength Pareto Evolutionary Algorithm-II (SPEA-II) [16], Pareto Envelope based selection-II (PESA-II) =-=[17]-=-. Most of these approaches propose the use of a generational GA. Deb proposed an Elitist Steady State Multi-objective Evolutionary Algorithm (MOEA) [18] which attempts to maintain spread [15] while at... |

28 | GADO: A genetic algorithm for continuous design optimization
- Rasheed
- 1998
(Show Context)
Citation Context ...tions and may have higher selection pressure which is desirable when evaluations are very expensive. With good diversity maintenance, steady state GAs have done very well in several realistic domains =-=[1]-=-. Significant research has yet to be done in the area of steady state multi-objective GAs. We therefore decided to focus our research on this area. The area of multi-objective optimization using Evolu... |

20 |
Design of truss-structures for minimum weight using genetic algorithms
- Gulati
- 2001
(Show Context)
Citation Context ...equal to the number of dimensions of the problems. 1. Population size: For OEGADO and OSGADO the population size was set to 10*ndim. For NSGA-II the population size was fixed to 100 as recommended in =-=[19]-=-. 2. Number of objective evaluations: Since the three methods work differently the number of objective evaluations is computed differently. The number of objective evaluations for OEGADO and OSGADO ac... |

18 |
GA-based decision support system for multi-criteria, optimization
- Tanaka
- 1995
(Show Context)
Citation Context ...ty of these problems varies from fairly simple to difficult. The problems chosen from the benchmark domains are BNH used by Binh and Korn [10], SRN used by Srinivas, Deb [11], TNK suggested by Tanaka =-=[12]-=- and OSY used by Osyczka, Kundu [13]. The problems chosen from the engineering domains are Two-Bar Truss Design used by Deb [14] and Welded Beam design used by Deb [14]. All these problems are constra... |

17 | Informed operators: Speeding up genetic-algorithmbased design optimization using reduced models
- Rasheed, Hirsh
- 2000
(Show Context)
Citation Context ... have independent populations. They exchange information about their respective objectives every certain number of iterations. In our implementation, we have used the idea of informed operators (IOs) =-=[4]-=-. The main idea of the IOs is to replace pure randomness in traditional GA operators with decisions that are guided by reduced models formed using the methods presented in [5, 6, 7]. The reduced model... |

14 | Learning to be selective in genetic-algorithm-based design optimization
- Rasheed, Hirsh
- 1999
(Show Context)
Citation Context ...s multiple objectives in a sequence switching at certain intervals between objectives. Our methods can be viewed as multi-objective transformations of GADO (Genetic Algorithm for Design Optimization) =-=[1, 2]-=-. GADO is a GA that was designed with the goal of being suitable for the use in engineering design. It uses new operators andsConstrained Multi-Objective Optimization Using Steady State Genetic Algori... |

12 | Comparison of Methods for Using Reduced Models to Speed up Design Optimization
- Rasheed, Vattam, et al.
- 2002
(Show Context)
Citation Context ...informed operators (IOs) [4]. The main idea of the IOs is to replace pure randomness in traditional GA operators with decisions that are guided by reduced models formed using the methods presented in =-=[5, 6, 7]-=-. The reduced models are approximations of the fitness function, formed using some approximation techniques, such as least squares approximation [5, 7, 8]. These functional approximations are then use... |

9 | An incremental-approximate-clustering approach for developing dynamic reduced models for design optimization
- Rasheed
- 2002
(Show Context)
Citation Context ...informed operators (IOs) [4]. The main idea of the IOs is to replace pure randomness in traditional GA operators with decisions that are guided by reduced models formed using the methods presented in =-=[5, 6, 7]-=-. The reduced models are approximations of the fitness function, formed using some approximation techniques, such as least squares approximation [5, 7, 8]. These functional approximations are then use... |

9 | Constraint method-based evolutionary algorithm (CMEA) for multi-objective optimization
- Ranjithan, Chetan, et al.
- 2001
(Show Context)
Citation Context ...PESA-II) [17]. Most of these approaches propose the use of a generational GA. Deb proposed an Elitist Steady State Multi-objective Evolutionary Algorithm (MOEA) [18] which attempts to maintain spread =-=[15]-=- while attempting to converge to the true Pareto-optimal front. This algorithm requires sorting of the population for every new solution formed thereby increasing its time complexity. Very high time c... |

6 | Mechanical Component Design for Multiple Objectives Using Elitist Non-Dominated Sorting GA
- Deb, A, et al.
- 2000
(Show Context)
Citation Context ... and Korn [10], SRN used by Srinivas, Deb [11], TNK suggested by Tanaka [12] and OSY used by Osyczka, Kundu [13]. The problems chosen from the engineering domains are Two-Bar Truss Design used by Deb =-=[14]-=- and Welded Beam design used by Deb [14]. All these problems are constrained multi-objective problems. Table 1 shows the variable bounds, objective functions and constraints for all these problems.sCo... |

1 | Comparison of methods for developing dynamic reduced models for design optimization
- Rasheed, Vattam, et al.
- 2002
(Show Context)
Citation Context ...informed operators (IOs) [4]. The main idea of the IOs is to replace pure randomness in traditional GA operators with decisions that are guided by reduced models formed using the methods presented in =-=[5, 6, 7]-=-. The reduced models are approximations of the fitness function, formed using some approximation techniques, such as least squares approximation [5, 7, 8]. These functional approximations are then use... |

1 |
A multi-objective Evolution Strategy for constrained optimization Problems
- MOBES
- 1997
(Show Context)
Citation Context ... problems from the engineering domains. The degree of difficulty of these problems varies from fairly simple to difficult. The problems chosen from the benchmark domains are BNH used by Binh and Korn =-=[10]-=-, SRN used by Srinivas, Deb [11], TNK suggested by Tanaka [12] and OSY used by Osyczka, Kundu [13]. The problems chosen from the engineering domains are Two-Bar Truss Design used by Deb [14] and Welde... |

1 |
A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm
- Osycza, Kundu
- 1995
(Show Context)
Citation Context ...rly simple to difficult. The problems chosen from the benchmark domains are BNH used by Binh and Korn [10], SRN used by Srinivas, Deb [11], TNK suggested by Tanaka [12] and OSY used by Osyczka, Kundu =-=[13]-=-. The problems chosen from the engineering domains are Two-Bar Truss Design used by Deb [14] and Welded Beam design used by Deb [14]. All these problems are constrained multi-objective problems. Table... |