Abstract
Many real-life difficult problems imply more than one optimization criterion and often require multiobjective optimization techniques. Among these techniques, nature-inspired algorithms, for instance, evolutionary algorithms, mimic various natural process and systems and succeed to perform appropriately for hard optimization problems. Besides, in chemistry, osmosis is the natural process of balancing the concentration of two solutions. This process takes place at the molecular level. Osmosis’s practical applications are multiple and target medicine, food safety, and engineering. However, osmosis process is not yet recognized as a rich source of inspiration for designing computational tools. At first glance, this well-known chemical process seems appropriate as a metaphor in nature-inspired computation as it can underlie the development of a search and optimization procedure. In this paper, we develop a novel algorithm called OSMIA (Osmosis inspired Algorithm) for multiobjective optimization problems. The proposed algorithm is inspired by the well-known physio-chemical osmosis process. For validation purposes, we have realized a case study in that we compared our proposed algorithm with the state-of-art algorithm NSGAII using some well known test problems. The conclusions of the case study emphasize the strengths of the proposed novel OSMIA algorithm.
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Acknowledgements
The authors gratefully acknowledge the financial support provided by the Romanian National Authority for Scientific Research, CNCS – UEFISCDI, under the Bridge Grant PN-III-P2-2.1-BG-2016-0302.
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Rotar, C., Iantovics, L.B., Arik, S. (2017). A Novel Osmosis-Inspired Algorithm for Multiobjective Optimization. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_9
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DOI: https://doi.org/10.1007/978-3-319-70093-9_9
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