Synergistic fibroblast optimization

Abstract

Movement is the main characteristic of living species and is the major source of biologically inspired computational systems. The migration of cell organism can take the form of either movement of cells or movement within cells, and it is also capable of changing the shape as a result of reversible or irreversible contraction. This paper simulates synergistic fibroblast optimization (SFO), a multi-agent heuristic technique that models the migration and methodical behavior of the fibroblast. The proposed SFO technique exhibits the role of fibroblast in dermal wound healing process, by migrating the individual cell through the connective tissue, and synthesizes the collagen in the extracellular matrix for the new tissue formation during wound healing. Compared to the related technique particle swarm optimization (PSO), SFO produces better results in terms of both accuracy and performance. An analysis of the proposed algorithm indicates that the two most important factors contributing to SFO effectiveness are fibroblast collaborative nature and goal-oriented feature. The results suggest that SFO is a promising new optimization technique, which may be particularly applicable to find optimal maxima or minima, among the candidate solutions in the nonlinear complicated optimization problem.