A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems

  1. E. Filatovas 1
  2. O. Kurasova 1
  3. J.L. Redondo 2
  4. J. Fernández 3
  1. 1 Vilnius University, Lituania
  2. 2 Universidad de Almería, España
  3. 3 Universidad de Murcia, España
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Revista:
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ISSN: 1863-8279,  1134-5764

Año de publicación: 2020

Volumen: 28

Número: 2

Páginas: 402-423

Tipo: Artículo

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Resumen

Most evolutionary multiobjective optimization algorithms are designed to approximate the entire Pareto front. During the last decade, a series of preference-based evolutionary algorithms have been developed, where a part of the Pareto front is approximated by incorporating the preferences of a Decision Maker. However, only a few such algorithms are able to obtain well-distributed solutions covering the complete “region of interest” that is determined by a reference point. In this paper, a preference-based evolutionary algorithm for approximating the region of interest is proposed. It is based on the state-of-the-art genetic algorithm NSGA-II and the CHIM approach introduced in the NBI method which is used to obtain uniformly distributed solutions in the region of interest. The efficiency of the proposed algorithm has been experimentally evaluated and compared to other state-of-the-art multiobjective preference-based evolutionary algorithms by solving a set of multiobjective optimization benchmark problems. It has been shown that the incorporation of the Decision Maker’s preferences and the CHIM approach into the NSGA-II algorithm allows approximating the whole region of interest accurately while maintaining a good distribution of the obtained solutions.

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Información de financiación

The research work of E. Filatovas was funded by a Grant (no. S-MIP-17-67) from the Research Council of Lithuania. The research work of J. L. Redondo and J. Fern?ndez was funded by Grants from the Spanish Ministry of Economy and Competitiveness (MTM2015-70260-P, TIN2015-66680-C2-1-R, RTI2018-095993-B-100), Fundaci?n S?neca (The Agency of Science and Technology of the Region of Murcia, 19241/PI/14 and 20817/PI/18), Junta de Andaluc?a (P12-TIC301, UAL18-TIC-A020-B), in part financed by the European Regional Development Fund (ERDF).

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