文献类型：会议论文

英文题名：A Worst-Case Analysis of Constraint-Based Algorithms for Exact Multi-objective Combinatorial Optimization

作者：Guo, Jianmei[1];Blais, Eric[2];Czarnecki, Krzysztof[2];van Beek, Peter[2]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China;[2]Univ Waterloo, Waterloo, ON, Canada

会议论文集：30th Canadian Conference on Artificial Intelligence (AI)

会议日期：MAY 16-19, 2017

会议地点：Edmonton, CANADA

语种：英文

外文关键词：Pareto principle - Logic programming - Benchmarking - Combinatorial optimization

摘要：In a multi-objective combinatorial optimization (MOCO) problem, multiple objectives must be optimized simultaneously. In past years, several constraint-based algorithms have been proposed for finding Pareto-optimal solutions to MOCO problems that rely on repeated calls to a constraint solver. Understanding the properties of these algorithms and analyzing their performance is an important problem. Previous work has focused on empirical evaluations on benchmark instances. Such evaluations, while important, have their limitations. Our paper adopts a different, purely theoretical approach, which is based on characterizing the search space into subspaces and analyzing the worst-case performance of a MOCO algorithm in terms of the expected number of calls to the underlying constraint solver. We apply the approach to two important constraint-based MOCO algorithms. Our analysis reveals a deep connection between the search mechanism of a constraint solver and the exploration of the search space of a MOCO problem.