Geometric Particle Swarm Optimization for Multi-objective Optimization Using Decomposition
Multi-objective evolutionary algorithms (MOEAs) based on decomposition are aggregation-based algorithms which transform a multi-objective optimization problem (MOP) into several single-objective subproblems. Being effective, efficient, and easy to implement, Particle Swarm Optimization (PSO) has bec...
| Autores: | , |
|---|---|
| Tipo de recurso: | capítulo de libro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | México |
| Institución: | Universidad Autónoma Metropolitana |
| Repositorio: | Concentración de Recursos de Información Científica y Académica, UAM Cuajimalpa |
| Idioma: | inglés |
| OAI Identifier: | oai:ilitia.cua.uam.mx:123456789/474 |
| Acceso en línea: | http://ilitia.cua.uam.mx:8080/jspui/handle/123456789/474 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/cti/7 Inteligencia de enjambre Estructura de datos (Computación) Algoritmos computacionales |
| Sumario: | Multi-objective evolutionary algorithms (MOEAs) based on decomposition are aggregation-based algorithms which transform a multi-objective optimization problem (MOP) into several single-objective subproblems. Being effective, efficient, and easy to implement, Particle Swarm Optimization (PSO) has become one of the most popular single-objective optimizers for continuous problems, and recently it has been successfully extended to the multi-objective domain. However, no investigation on the application of PSO within a multi-objective decomposition framework exists in the context of combinatorial optimization. This is precisely the focus of the paper. More specifically, we study the incorporation of Geometric Particle Swarm Optimization (GPSO), a discrete generalization of PSO that has proven successful on a number of single-objective combinatorial problems, into a decomposition approach. We conduct experiments on manyobjective 1/0 knapsack problems i.e. problems with more than three objectives functions, substantially harder than multi-objective problems with fewer objectives. The results indicate that the proposed multi-objective GPSO based on decomposition is able to outperform two version of the wellknow MOEA based on decomposition (MOEA/D) and the most recent version of the non-dominated sorting genetic algorithm (NSGA-III), which are state-of-the-art multi-objective evolutionary approaches based on decomposition. |
|---|