DEMO-TDQL: An adaptive multi-objective optimization algorithm
Authors:
- 1 June 2013
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 3095-3102
Abstract
An adaptive memetic algorithm incorporates an adaptive selection of memes (units of cultural transmission) from a meme-pool to improve the cultural characteristics of the individual member of a population-based search algorithm. The paper proposes an extension of Multi-objective Optimization realized with Differential Evolution algorithm by utilizing the composite benefits of Differential Evolution for Multi-objective Optimization (DEMO) for global search and Templocal refinementoral Difference Q-Learning (TDQL) for local refinement. Computer simulations performed on a well known set of 23 benchmark functions reveal that the proposed algorithm outperforms its competitors with respect to inverted generational distance, spacing and error ratio.Keywords
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