ABSTRACT
This chapter presents the Central Force Optimization (CFO) metaphor, analysis of probe distribution and decision space (DS), the CFO algorithm with discussion on different parameters of CFO algorithms, convergence analysis, variants of CFO, and finally a few applications to engineering problems. CFO is a relatively new meta-heuristic optimization algorithm proposed by Formato. CFO is basically a maximization algorithm applied to an optimization problem where the criterion of optimality is defined by an objective function. The CFO algorithm is a deterministic and meta-heuristic algorithm for solving multidimensional optimization problem. The main issue in CFO is the selection or estimation of the parameter values. The CFO algorithm is sensitive to the initial probe distribution. Researchers have shown that the CFO algorithm is sensitive to distinctive topological distributions and the neighborhood topologies can be mapped to certain mathematical functions. The chapter also presents variants of CFO such as: simple CFO, extended CFO, pseudo-random CFO, parameter-free CFO, improved CFO, binary CFO and hybrid CFO.
