×

DEMO: Differential evolution for multiobjective optimization. (English) Zbl 1109.68633

Coello Coello, Carlos A. (ed.) et al., Evolutionary multi-criterion optimization. Third international conference, EMO 2005, Guanajuato, Mexico, March 9–11, 2005. Proceedings. Berlin: Springer (ISBN 3-540-24983-4/pbk). Lecture Notes in Computer Science 3410, 520-533 (2005).
Summary: Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications. In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective optimization based on DE. DEMO combines the advantages of DE with the mechanisms of Pareto-based ranking and crowding distance sorting, used by state-of-the-art evolutionary algorithms for multiobjective optimization. DEMO is implemented in three variants that achieve competitive results on five ZDT test problems.
For the entire collection see [Zbl 1069.68002].

MSC:

68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
90C29 Multi-objective and goal programming
90C59 Approximation methods and heuristics in mathematical programming

Software:

SPEA2
PDFBibTeX XMLCite
Full Text: DOI