Timing the Decision Support for Real-World Many-Objective Optimization Problems
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Lately, there is growing emphasis on improving the scalability of multi-objective evolutionary algorithms (MOEAs) so that many-objective problems (characterized by more than three objectives) can be effectively dealt with. Alternatively, the utility of integrating decision maker’s (DM’s) preferences into the optimization process so as to target some most preferred solutions by the DM (instead of the whole Pareto-optimal front), is also being increasingly recognized. The authors here, have earlier argued that despite the promises in the latter approach, its practical utility may be impaired by the lack of—objectivity, repeatability, consistency, and coherence in the DM’s preferences. To counter this, the authors have also earlier proposed a machine learning based decision support framework to reveal the preference-structure of objectives. Notably, the revealed preference-structure may be sensitive to the timing of application of this framework along an MOEA run. In this paper the authors counter this limitation, by integrating a termination criterion with an MOEA run, towards determining the appropriate timing for application of the machine learning based framework. Results based on three real-world many-objective problems considered in this paper, highlight the utility of the proposed integration towards an objective, repeatable, consistent, and coherent decision support for many-objective problems.
KeywordsDecision Support Dissimilarity Measure Relative Percentage Difference Correlation Strength Multiple Criterion Decision Making
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