The Particle swarm optimization (PSO) is a global search optimization algorithm having extensive usage across different engineering and scientific domains owing to its effective and simple performance. However, it stagnates at local optimal solution and has slow convergence behavior. To minimize such limitations, an enhanced version of the PSO algorithm is proposed in this work, known as F-PSO. The F-PSO algorithm integrates the established Particle Swarm Optimization with the Fibonacci sequence, thereby improving its capability to discover global optimal solutions by employing multiple initializations. The F-PSO will enhance the search space by empowering it with an exhaustive search and thus enhancing stability and convergence rate. The effectiveness of F-PSO is examined on different constrained and un-constrained benchmark functions thus depicting perfect trade-off between exploration and exploitation behavior.