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METCO: A PARALLEL PLUGIN-BASED FRAMEWORK FOR MULTI-OBJECTIVE OPTIMIZATION

COROMOTO LEÓN

Dpto. Estadística, I. O. y Computación, Universidad de La Laguna, La Laguna, 38271, Santa Cruz de Tenerife, Spain

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GARA MIRANDA

Dpto. Estadística, I. O. y Computación, Universidad de La Laguna, La Laguna, 38271, Santa Cruz de Tenerife, Spain

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, and

CARLOS SEGURA

Dpto. Estadística, I. O. y Computación, Universidad de La Laguna, La Laguna, 38271, Santa Cruz de Tenerife, Spain

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https://doi.org/10.1142/S0218213009000275Cited by:44 (Source: Crossref)

Abstract

This paper presents a parallel framework for the solution of multi-objective optimization problems. The framework implements some of the best known multi-objective evolutionary algorithms. The plugin-based architecture of the framework minimizes the end user effort required to incorporate their own problems and evolutionary algorithms, and facilitates tool maintenance. A wide variety of configuration options can be specified to adapt the software behavior to many different parallel models. An innovation of the framework is that it provides a self-adaptive parallel model that is based on the cooperation of a set of evolutionary algorithms. The aim of the new model is to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths of one algorithm can compensate for the weaknesses of another. The model proposed is a hybrid algorithm that combines a parallel island-based scheme with a hyperheuristic approach. The model grants more computational resources to those algorithms that show a more promising behavior. The flexibility and efficiency of the framework were tested and demonstrated by configuring standard and self-adaptive models for test problems and real-world applications.

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