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## Particle Swarm Optimization to Solve Optimization Problems

### Citations

1814 | A Fast Elitist Multi-Objective Genetic Algorithm: NSGA-II.
- Deb, Pratap, et al.
- 2000
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Citation Context ...g several test functions taken from the specialized literature. Results were compared against the micro-GA for multiobjective optimization [3], the Nondominated Sorting Genetic Algorithm II (NSGA II) =-=[5]-=-, the Pareto Archived Evolution Strategy (PAES) [9], and the Multiobjective Particle Swarm Optimization (MOPSO) [2]. Besides graphical comparisons, we adopted three metrics to compare our results [4].... |

845 |
Evolutionary Algorithms for Solving Multiobjective Problems,
- Coello, Veldhuizen, et al.
- 2002
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Citation Context ... [5], the Pareto Archived Evolution Strategy (PAES) [9], and the Multiobjective Particle Swarm Optimization (MOPSO) [2]. Besides graphical comparisons, we adopted three metrics to compare our results =-=[4]-=-. Figure 1 shows a sample result for one of the problems used. In general, our algorithm had a good convergence rate to the true Pareto front, and the results indicate that our approach is a viable al... |

362 |
Parameter selection in particle swarm optimization,”
- Eberhart, Shi
- 1998
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Citation Context ... PSO, as to make this algorithm competitive with the stateof-the-art approaches in the area [1]. 1.2 Our Proposed Approach For computing the velocity of a particle, we used the expression proposed in =-=[12]-=-: 6 87 :9; 6 87<>= +?;@-A#CBD+ E; %GFIHJLKLM N7?O * 87 <P= /Q;R@-ASCBS/ E; FLHTJLKLM 87UO * 87 where 6 87 is the velocity of the LB dimension, = + and = / are two values... |

321 | Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy.
- Knowles, Corne
- 2000
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Citation Context ... literature. Results were compared against the micro-GA for multiobjective optimization [3], the Nondominated Sorting Genetic Algorithm II (NSGA II) [5], the Pareto Archived Evolution Strategy (PAES) =-=[9]-=-, and the Multiobjective Particle Swarm Optimization (MOPSO) [2]. Besides graphical comparisons, we adopted three metrics to compare our results [4]. Figure 1 shows a sample result for one of the prob... |

202 | Stochastic ranking for constrained evolutionary optimization,”
- Runarsson, Yao
- 2000
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Citation Context ...s to choose as a leader to the particle that, even when infeasible, lies closer to the feasible region. To evaluate the performance of the proposed approach we used the 13 test functions described in =-=[11]-=-, and we compared our results with respect to three constraint-handling 1 This mechanism is inspired on [6]. techniques that are representative of the state-of-the-art in the area: Stochastic Ranking ... |

187 | Theoretical and Numerical Constraint-Handling Techniques Used With Evolutionary Algorithms:
- Coello, A
- 2002
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Citation Context ...n goal of this initial research was to develop a relatively simple mechanism to incorporate constraints into PSO, as to make this algorithm competitive with the stateof-the-art approaches in the area =-=[1]-=-. 1.2 Our Proposed Approach For computing the velocity of a particle, we used the expression proposed in [12]: 6 87 :9; 6 87<>= +?;@-A#CBD+ E; %GFIHJLKLM N7?O * 87 <P= /Q;R@-ASCBS/ ... |

119 | Mopso: A proposal for multiple objective particle swarm optimization.
- Coello, Lechuga
- 2002
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Citation Context ...ressure. This sort of scheme is a novel proposal to solve multiobjective optimization problems using PSO. Also, note that this algorithm does not use an external population (as other recent proposals =-=[2, 6]-=-), since elitism in this case is an emergent process derived from the migration of leaders. The complete execution process of our algorithm can be divided in three stages: initialization, flight and g... |

59 | A multi-objective algorithm based upon particle swarm optimization, an efficient data structure and turbulence,”
- Fieldsend, Singh
- 2002
(Show Context)
Citation Context ...valuate the performance of the proposed approach we used the 13 test functions described in [11], and we compared our results with respect to three constraint-handling 1 This mechanism is inspired on =-=[6]-=-. techniques that are representative of the state-of-the-art in the area: Stochastic Ranking (SR) [11], the Homomorphous Maps (HM) [10], and the Adaptive Segregational Constraint Handling Evolutionary... |

28 |
ASCHEA: new results using adaptive segregational constraint handling.
- Hamida, Schoenauer
- 2002
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Citation Context ...e representative of the state-of-the-art in the area: Stochastic Ranking (SR) [11], the Homomorphous Maps (HM) [10], and the Adaptive Segregational Constraint Handling Evolutionary Algorithm (ASCHEA) =-=[7]-=-. Due to space limitations, in Table 1 we only present a comparison of our results against Stochastic Ranking, which is the most competitive of the approaches previously indicated. The results obtaine... |

27 |
and Zbigniew Michalewicz. Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
- Koziel
- 1999
(Show Context)
Citation Context ...pect to three constraint-handling 1 This mechanism is inspired on [6]. techniques that are representative of the state-of-the-art in the area: Stochastic Ranking (SR) [11], the Homomorphous Maps (HM) =-=[10]-=-, and the Adaptive Segregational Constraint Handling Evolutionary Algorithm (ASCHEA) [7]. Due to space limitations, in Table 1 we only present a comparison of our results against Stochastic Ranking, w... |

9 |
Coello Coello, Gregorio Toscano Pulido, A microgenetic algorithm for multi-objective optimization,
- Carlos
- 1993
(Show Context)
Citation Context ...proach is shown as crosses. This algorithm was validated using several test functions taken from the specialized literature. Results were compared against the micro-GA for multiobjective optimization =-=[3]-=-, the Nondominated Sorting Genetic Algorithm II (NSGA II) [5], the Pareto Archived Evolution Strategy (PAES) [9], and the Multiobjective Particle Swarm Optimization (MOPSO) [2]. Besides graphical comp... |