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A parallel multiple reference point approach for multi-objective optimization. (English) Zbl 1188.90237
Summary: This paper presents a multiple reference point approach for multi-objective optimization problems of discrete and combinatorial nature. When approximating the Pareto Frontier, multiple reference points can be used instead of traditional techniques. These multiple reference points can easily be implemented in a parallel algorithmic framework. The reference points can be uniformly distributed within a region that covers the Pareto Frontier. An evolutionary algorithm is based on an achievement scalarizing function that does not impose any restrictions with respect to the location of the reference points in the objective space. Computational experiments are performed on a bi-objective flow-shop scheduling problem. Results, quality measures as well as a statistical analysis are reported in the paper.

90C29 Multi-objective and goal programming
68W10 Parallel algorithms in computer science
90C59 Approximation methods and heuristics in mathematical programming
90B35 Deterministic scheduling theory in operations research
Full Text: DOI
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