Multiobjective optimization is of increasing importance in various fields and has very broad applications. The purpose of this paper is to describe a novel multiobjective optimization algorithm-opposition-based multi-objective differential evolution algorithm(OMODE). In the paper, OMODE uses the opposition-based population to generate the initial population of points, The important scaling factor is controlled by self-adaptive method. Performance of OMODE is demonstrated with a set of benchmark test functions and Earth-Mars double transfer problem. The results show that OMODE achieves better performance than other methods. © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Peng, L., Wang, Y., & Dai, G. (2008). A novel opposition-based multi-objective differential evolution algorithm for multi-objective optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5370 LNCS, pp. 162–170). https://doi.org/10.1007/978-3-540-92137-0_18
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