Synergizing Improved MPSO algorithm and FDB method for Improved Optimization

Abstract

Optimization plays a pivotal role in solving complex problems across various domains. The Particle Swarm Optimization (PSO) algorithm, inspired by social behaviors in nature, has gained popularity for its simplicity and effectiveness. However, conventional PSO faces challenges such as premature convergence and limited exploration capabilities, especially in high-dimensional and complex optimization landscapes. To address these limitations, this research introduces a hybrid algorithm that synergizes the Modified Particle Swarm Optimization (MPSO) and the Fitness-Distance Balance (FDB) method. The MPSO enhances PSO by incorporating mechanisms to improve population diversity and balance exploration and exploitation. The FDB method further complements this by integrating fitness value and spatial distance metrics, promoting a diverse solution space and preventing premature convergence. The proposed MPSO-FDB algorithm was evaluated on benchmark functions of varying complexity and dimensions using MATLAB. Results demonstrate significant improvements in convergence speed, solution quality, and resilience compared to traditional PSO and other variants. The algorithm effectively balances exploration and exploitation, making it well-suited for high-dimensional optimization tasks. This paper underscores the potential of integrating FDB with MPSO, providing a scalable and robust approach to optimization challenges in engineering, economics, and artificial intelligence.