Nature solves complex problems in a simple manner. This inspired researchers to develop a mathematical model to solve real-world problems that are not solvable or require high-end resources. The particle swarm optimization (PSO) is a nature-inspired techniques that have solved numerous engineering optimization problems. Sometimes PSO suffers from slow and premature convergence due to a high rate of exploration and exploitation, respectively. To overcome this problem, this paper introduced a modified PSO algorithm with dynamic inertia weight control with the help of fitness to improve convergence. The proposed approach uses dynamic inertia weight control in PSO to improve balancing in exploration and exploitation. Due to a fitness-driven approach, the proposed variant is adaptable for the population and avoids premature convergence, which is usually a problem basic PSO faces. The proposed approach is named as dynamic inertia weight control using fitness metrics in the PSO Algorithm (DIW-PSO). The experimental results demonstrated that DIW-PSO is best suited for multimodal high-dimensional problems and in a dynamic environment.