A Novel Search Technique for Global Optimization

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 259)


Solving non-linear optimization with more accuracy has become a challenge for the researchers. Evolutionary global search techniques today are treated as the alternate paradigm over the traditional methods for their simplicity and robust nature. However, if an evolutionary problem is computationally burdened both the human efforts and time will be wasted. In this paper a much simpler and more robust optimization algorithm called Drosophila Food-Search Optimization (DFO) Algorithm is proposed. This new technique is based on the food search behavior of the fruit fly called ‘Drosophila’. In order to evaluate the efficiency and efficacy of the DFO-algorithm, a set of 20 unconstrained benchmark problems have been used. The numerical results confirms the supremacy of DFO over the algorithms namely Hybrid Ant Colony-Genetic Algorithm (GAAPI), Level-Set evolution and Latin squares Algorithm (LEA), which are reported as the most efficient algorithms in the recent literature.


Redundant search (RS) G-Protein-coupled-receptor (GPCR) Ligand and modified quadratic approximation (mQA) 


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Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of MathematicsNational Institute of TechnologySilcharIndia

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