Analyzing Inter-objective Relationships: A Case Study of Software Upgradability
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In the process of solving real-world multi-objective problems, many existing studies only consider aggregate formulations of the problem, leaving the relationships between different objectives less visited. In this study, taking the software upgradability problem as a case study, we intend to gain insights into the inter-objective relationships of multi-objective problems. First, we obtain the Pareto schemes by uniformly sampling a set of solutions within the Pareto front. Second, we analyze the characteristics of the Pareto scheme, which reveal the relationships between different objectives. Third, to estimate the inter-objective relationships for new upgrade requests, we build a predictive model, with a set of problem-specific features. Finally, we propose a reference based indicator, to assess the risk of applying single-objective algorithms to solve the multi-objective software upgradability problem. Extensive experimental results demonstrate that, the predictive models built with problem-specific features are able to predict both algorithm independent inter-objective relationships, as well as the algorithm performance specific indicator properly.
KeywordsPareto front Meta-learning Empirical analysis
This work is supported in part by the National Natural Science Foundation of China under Grants 61370144 and 61403057, in part by National Program on Key Basic Research Project under Grant 2013CB035906, and in part by the Fundamental Research Funds for the Central Universities under Grants DUT15TD37 and DUT16RC(4)62.
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