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

2006; Research India Publications; Volume: 2; Issue: 3 Linguagem: Inglês

10.5019/j.ijcir.2006.68

0974-1259

Carlos A. Coello Coello, Margarita Reyes-Sierra,

Evolutionary Algorithms and Applications

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 bio-inspired technique to other areas.One of them is multi-objective 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.Baumgartner et al. [6] fully connected single-objective no no no Lexicographic ordering Hu and Eberhart [24] ring single-objective no yes no rnd(0.5,1.0) Hu et al. [25] ring single-objective yes yes no rnd(0.5,1.0) Sub-Population approaches Parsopoulos et al. [49] fully connected single-objective yes no no Chow and Tsui [8] fully connected single-objective no no no Pareto-Based approaches Moore and Chapman [41] ring dominance no no no Ray and Liew [53] fully connected density estimator yes no no Fieldsend and Singh [21] fully connected dominance & yes no yes closeness Coello et al. [11, 12] fully connected density of solutions yes no yes Toscano and Coello [66] fully connected randomly no no no Srinivasan and Hou [61] fully connected niche count & no no