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Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art

Coello Coello, Carlos A., Reyes-Sierra, Margarita·2006·International Journal of Computational Intelligence Research

Abstract

Abstract- The success of the Particle Swarm Optimization (PSO) algorithm as a single-objective optimizer (mainly when dealing with continuous search spaces) has motivated researchers to extend the use of this bioinspired technique to other areas. One of them is multiobjective optimization. Despite the fact that the first proposal of a Multi-Objective Particle Swarm Optimizer (MOPSO) is over six years old, a considerable number of other algorithms have been proposed since then. This paper presents a comprehensive review of the various MOPSOs reported in the specialized literature. As part of this review, we include a classification of the approaches, and we identify the main features of each proposal. In the last part of the paper, we list some of the topics within this field that we consider as promising areas of future research. 1

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Metadata

Authors
Coello Coello, C. A., & Reyes-Sierra, M.
Venue
International Journal of Computational Intelligence Research
Published
2006
Volume
2
Issue
3
ISSN
  • 0973-1873 (ISSN-L)
  • 0974-1259
OpenAlex
W2033630480
Cited by
1,365
References
1
FWCI
43.74
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art. https://app.cochat.ai/papers/10.5019/j.ijcir.2006.68
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Coello Coello, C. A., & Reyes-Sierra, M. (2006). Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art. International Journal of Computational Intelligence Research. https://doi.org/10.5019/j.ijcir.2006.68

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