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An r-dominance-based preference multi-objective optimization for many-objective optimization
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DOI：10.1007/s00500-016-2098-x.png)
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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.
Keyword:
Preference multi-objective optimization
Artificial immune system
Preference rank
Many-objective problem
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