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An r-dominance-based preference multi-objective optimization for many-objective optimization

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Abstract

Evolutionary multi-objective optimization (EMO) algorithms have been used in finding a representative set of Pareto-optimal solutions in the past decade and beyond. However, most of Pareto domination-based multi-objective optimization evolutionary algorithms (MOEAs) are not suitable for many-objective optimization, in which, a good trade-off among many objectives becomes very difficult. In real-world applications, the fact is that the decision-maker is not interested in the overall Pareto-optimal front since the final decision is a unique or several solutions. So the decision-maker can incorporate his/her preferences into the search process of MOEAs to guide the search toward the preferred parts of the Pareto region rather than the whole Pareto-optimal region. In this paper, we hybridize the classical Pareto dominance principle with reference-based dominance and propose a reference-dominance-based preference multi-objective optimization algorithm (r-PMOA). The proposed method has been extensively compared with other recently proposed preference-based EMO approaches over several benchmark problems of multi-objective optimization having 2–10 objectives. The results of the experiment indicate that r-PMOA achieves competitive results.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 61373111, 61272279, 61103119 and 61203303); the Fundamental Research Funds for the Central Universities (Nos. K50511020014, K50510020011, K5051302049, and K5051302023); and the Provincial Natural Science Foundation of Shaanxi of China (No. 2014JM8321).

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Authors and Affiliations

  1. Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China

    Ruochen Liu, Xiaolin Song, Lingfen Fang & Licheng Jiao

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  1. Ruochen Liu
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  2. Xiaolin Song
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  3. Lingfen Fang
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  4. Licheng Jiao
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Correspondence to Ruochen Liu.

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Communicated by V. Loia.

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Liu, R., Song, X., Fang, L. et al. An r-dominance-based preference multi-objective optimization for many-objective optimization. Soft Comput 21, 5003–5024 (2017). https://doi.org/10.1007/s00500-016-2098-x

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