Performance analysis of Synergistic Fibroblast Optimization (SFO) algorithm
T T Dhivyaprabha, P. Subashini · 2017
In the recent scenarios, Computational Intelligence techniques are widely applied to solve complex scientific and mathematical problems which involve variety and huge volume of data. The characteristics of data have great influence on the behaviour of Nature Inspired Computing (NIC) algorithms to find optimal or near optimal solution while solving non-linear complicated problems. In this paper, the performance of the newly developed Synergistic Fibroblast Optimization (SFO) algorithm on solving different real world problems which encompass diverse sort of dataset has been investigated and demonstrated its effectiveness. The significant outcomes have revealed that the novel SFO algorithm is efficient and compatible with various types of data, computational methods and techniques to produce promising results in both qualitative and quantitative perspectives.