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Approximative solution methods for multiobjective combinatorial optimization. With discussion and a rejoinder by the authors. (English) Zbl 1148.90300
Summary: In this paper we present a review of approximative solution methods, that is, heuristics and metaheuristics designed for the solution of multiobjective combinatorial optimization problems (MOCO). First, we discuss questions related to approximation in this context, such as performance ratios, bounds, and quality measures. We give some examples of heuristics proposed for the solution of MOCO problems. The main part of the paper covers metaheuristics and more precisely non-evolutionary methods. The pioneering methods and their derivatives are described in a unified way. We provide an algorithmic presentation of each of the methods together with examples of applications, extensions, and a bibliographic note. Finally, we outline trends in this area.
MSC:
90-02Research monographs (optimization)
90C27Combinatorial optimization
90C29Multi-objective programming; goal programming
90C59Approximation methods and heuristics
Software:
PAES; EMOO; MACS-VRPTW
References:
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