Bio-Inspired Algorithms and Preferences for Multi-objective Problems
Multi-objective optimization evolutionary algorithms have been applied to solve many real-life decision problems. Most of them require the management of trade-offs between multiple objectives. Reference point approaches highlight a preferred set of solutions in relevant areas of Pareto frontier and support the decision makers to take more confidence evaluation. This paper extends some well-known algorithms to work with collective preferences and interactive techniques. In order to analyse the results driven by the online reference points, two new performance indicators are introduced and tested against some synthetic problem.
KeywordsCollective intelligence Preferences Reference points Evolutionary multi-objective optimization algorithms
This work was partially funded by CNPq BJT Project 407851/2012-7, FAPERJ APQ1 Project 211.500/2015, FAPERJ APQ1 Project 211.451/2015.
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