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## On the Effect of Selection and Archiving Operators in Many-Objective Particle Swarm Optimisation

### Citations

3768 | A modified particle swarm optimizer
- Shi, Eberhart
- 1998
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Citation Context ...quires prior specific permission and/or a fee. GECCO’13, July 6–10, 2013, Amsterdam, The Netherlands. Copyright 2013 ACM 978-1-4503-1963-8/13/07 ...$15.00. 1. INTRODUCTION Since its inception in 1995 =-=[14]-=- the particle swarm optimisation (PSO) heuristic has gained rapid popularity as a technique to facilitate single objective optimisation. Like the standard evolutionary algorithm (EA) methods of geneti... |

845 |
Evolutionary Algorithms for Solving Multi-Objective Problems
- Coello, Veldhuizen, et al.
- 2002
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Citation Context ...gion being accepted – which puts a limit on the best IGD attainable. Optimiser performance is tracked using the widely used generational distance (GD) and inverse generational distance (IGD) measures =-=[3]-=-, which quantify the convergence to the Pareto front, and the spread and convergence to the Pareto front respectively. For the calculation of GD and IGD, 10000 samples from the relevant F were generat... |

661 | A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Paper presented at the sixth international conference on parallel problem solving from nature, - Deb - 2000 |

119 | Mopso: A proposal for multiple objective particle swarm optimization.
- Coello, Lechuga
- 2002
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Citation Context ...ious iteration of the optimiser, and uses this to adjust the particle’s position in the current iteration. A decade ago (circa 2002), researchers began publishing multi-objective (MO) variants of PSO =-=[2, 11, 13, 18]-=- (although an unpublished paper on the area exists from 1999 [15]), typically referred to as MOPSO algorithms. Since these works there has been a large growth in the number and range of MOPSO algorith... |

112 | Scalable multi-objective optimization test problems, in:
- Deb, Thiele, et al.
- 2002
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Citation Context ... selection protocols for determining the particular global best and personal best to be chosen for each particle in each generation, across the DTLZ1-4 test functions with recommended parametrisation =-=[7]-=-, for objectives = {5, 10, 15, 20}. The protocols were: FR, KO, CDAS-R, CD, AR, SR, and the baseline of random selection from the non-dominated sets (i.e., Paretobased selection), which we denote by R... |

85 | Multiobjective optimization using dynamic neighborhood particle swarm optimization,”
- Hu, Eberhart
- 2002
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Citation Context ...ious iteration of the optimiser, and uses this to adjust the particle’s position in the current iteration. A decade ago (circa 2002), researchers began publishing multi-objective (MO) variants of PSO =-=[2, 11, 13, 18]-=- (although an unpublished paper on the area exists from 1999 [15]), typically referred to as MOPSO algorithms. Since these works there has been a large growth in the number and range of MOPSO algorith... |

75 | Multi-objective particle swarm optimizers: A survey of the state-of-the-art,
- Reyes-Sierra, Coello
- 2006
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Citation Context ...ence operators, etc.). As the number of distinct MOPSOs has grown, a number of papers have provided overviews of the range of approaches that can be taken, along with some empirical comparisons (e.g. =-=[10, 16, 17, 19]-=-). However there has been relatively little work thus far examining many-objective PSO performance (i.e., on problems with four or more objectives) [21, 5]. In this paper we are concerned with the per... |

70 | Particle swarm optimization method in multiobjective problems”.
- Parsopoulos, Vrahatis
- 2002
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Citation Context ...ious iteration of the optimiser, and uses this to adjust the particle’s position in the current iteration. A decade ago (circa 2002), researchers began publishing multi-objective (MO) variants of PSO =-=[2, 11, 13, 18]-=- (although an unpublished paper on the area exists from 1999 [15]), typically referred to as MOPSO algorithms. Since these works there has been a large growth in the number and range of MOPSO algorith... |

59 |
Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms,
- Bentley, Wakefield
- 1998
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Citation Context ...ur or more objectives) [21, 5]. In this paper we are concerned with the performance of the PSO heuristic on many-objective problems, and the effectiveness of different quality measures in this domain =-=[1, 6, 8, 9, 20]-=- when used within PSO. As such, we limit the variation of the baseline optimiser used to a simple PSO, and vary purely the use of these measures for archive maintenance and for selection. The paper pr... |

59 | A multi-objective algorithm based upon particle swarm optimization, an efficient data structure and turbulence,”
- Fieldsend, Singh
- 2002
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33 | Techniques for highly multiobjective optimisation: Some nondominated points are better than others,
- Corne, Knowles
- 2007
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Citation Context ...sed selection), which we denote by RR. As the first six protocols rank the non-dominated sets maintained, these rankings were used to select guides based on tournament selection of size 5 (as used in =-=[4]-=-, where a subset of the manyobjective operators considered here were compared in terms of their ability to discriminate between non-dominated solutions, and empirically within a GA). Element-wise trun... |

23 |
Application of Particle Swarm to Multiobjective Optimization,
- Moore, Chapman
- 1999
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Citation Context ...tion in the current iteration. A decade ago (circa 2002), researchers began publishing multi-objective (MO) variants of PSO [2, 11, 13, 18] (although an unpublished paper on the area exists from 1999 =-=[15]-=-), typically referred to as MOPSO algorithms. Since these works there has been a large growth in the number and range of MOPSO algorithms published in the literature, which has largely tracked the gro... |

21 |
An investigation on preference order ranking scheme for multiobjective evolutionary optimization,”
- Pierro, Soon-Thiam, et al.
- 2007
(Show Context)
Citation Context ...ur or more objectives) [21, 5]. In this paper we are concerned with the performance of the PSO heuristic on many-objective problems, and the effectiveness of different quality measures in this domain =-=[1, 6, 8, 9, 20]-=- when used within PSO. As such, we limit the variation of the baseline optimiser used to a simple PSO, and vary purely the use of these measures for archive maintenance and for selection. The paper pr... |

12 | Multi-objective optimisation based on relation favour
- Drechsler, Drechsler, et al.
- 2001
(Show Context)
Citation Context ...ur or more objectives) [21, 5]. In this paper we are concerned with the performance of the PSO heuristic on many-objective problems, and the effectiveness of different quality measures in this domain =-=[1, 6, 8, 9, 20]-=- when used within PSO. As such, we limit the variation of the baseline optimiser used to a simple PSO, and vary purely the use of these measures for archive maintenance and for selection. The paper pr... |

7 | Using a Distance Metric to Guide PSO Algorithms for Many-Objective Optimization
- Wickramasinghe, Li
- 2009
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Citation Context ...th some empirical comparisons (e.g. [10, 16, 17, 19]). However there has been relatively little work thus far examining many-objective PSO performance (i.e., on problems with four or more objectives) =-=[21, 5]-=-. In this paper we are concerned with the performance of the PSO heuristic on many-objective problems, and the effectiveness of different quality measures in this domain [1, 6, 8, 9, 20] when used wit... |

3 |
Multi-objective particle swarm optimisation methods
- Fieldsend
- 2004
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Citation Context ...ence operators, etc.). As the number of distinct MOPSOs has grown, a number of papers have provided overviews of the range of approaches that can be taken, along with some empirical comparisons (e.g. =-=[10, 16, 17, 19]-=-). However there has been relatively little work thus far examining many-objective PSO performance (i.e., on problems with four or more objectives) [21, 5]. In this paper we are concerned with the per... |

2 |
Empirical comparison of MOPSO methods: guide selection and diversity preservation
- Padhye, Branke, et al.
- 2009
(Show Context)
Citation Context ...ence operators, etc.). As the number of distinct MOPSOs has grown, a number of papers have provided overviews of the range of approaches that can be taken, along with some empirical comparisons (e.g. =-=[10, 16, 17, 19]-=-). However there has been relatively little work thus far examining many-objective PSO performance (i.e., on problems with four or more objectives) [21, 5]. In this paper we are concerned with the per... |

1 |
Using Different Many-Objective Techniques in Particle Swarm Optimization for Many Objective Problems: An Empirical Study
- Carvalho, Pozo
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Citation Context ...th some empirical comparisons (e.g. [10, 16, 17, 19]). However there has been relatively little work thus far examining many-objective PSO performance (i.e., on problems with four or more objectives) =-=[21, 5]-=-. In this paper we are concerned with the performance of the PSO heuristic on many-objective problems, and the effectiveness of different quality measures in this domain [1, 6, 8, 9, 20] when used wit... |

1 |
Two novel approaches for many-objective optimization
- Garza-Fabre, Toscano-Pulido, et al.
- 2010
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Citation Context ... of solutions it will then give greater and greater preference towards the edge in comparison with SR (as it prefers solutions that are in the centre of its ordering, rather than the geometric centre =-=[12]-=-). In terms of Figure 3 and the maintenance approaches, the analysis is much simpler. CDAS0.3 is seen to perform significantly and substantially better than all seven other approaches across DTLZ1, 3 ... |

1 |
Comparison of archiving methods in multi-objective particle swarm optimization (MOPSO): empirical study
- Padhye
- 2009
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1 |
Controlling Dominance Area of Solutions in Multiobjective Evolutionary Algorithms and Performance Analysis on Multiobjective 0/1 Knapsack Problems
- Sato, Aguirre, et al.
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