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Adaptive Multiobjective Memetic Optimization

Adaptive Multiobjective Memetic Optimization

Hieu V. Dang, Witold Kinsner
Copyright: © 2016 |Volume: 10 |Issue: 4 |Pages: 38
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781466689671|DOI: 10.4018/IJCINI.2016100102
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MLA

Dang, Hieu V., and Witold Kinsner. "Adaptive Multiobjective Memetic Optimization." IJCINI vol.10, no.4 2016: pp.21-58. http://doi.org/10.4018/IJCINI.2016100102

APA

Dang, H. V. & Kinsner, W. (2016). Adaptive Multiobjective Memetic Optimization. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 10(4), 21-58. http://doi.org/10.4018/IJCINI.2016100102

Chicago

Dang, Hieu V., and Witold Kinsner. "Adaptive Multiobjective Memetic Optimization," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 10, no.4: 21-58. http://doi.org/10.4018/IJCINI.2016100102

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Abstract

Multiobjective memetic optimization algorithms (MMOAs) are recently applied to solve nonlinear optimization problems with conflicting objectives. An important issue in an MMOA is how to identify the relative best solutions to guide its adaptive processes. In this paper, the authors introduce a framework of adaptive multiobjective memetic optimization algorithms (AMMOA) with an information theoretic criterion for guiding the adaptive selection, clustering, local learning processes, and a robust stopping criterion of AMMOA. The implementation of AMMOA is applied to several benchmark test problems with remarkable results. The paper also presents the application of AMMOA in designing an optimal image watermarking to maximize the quality of the watermarked images and the robustness of the watermark.

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