Advanced Particle Swarm Optimization: An Innovative Approach Towards Addressing Constrained Optimization Challenges

A refined version of the traditional particle swarm optimization (PSO) method for handling constraint-based optimization is described in this paper. Numerous academic fields, including economics, finance, a nd engineering, frequently encounter bounded optimization challenges. The presence of constraints that restrict the possible search area makes solving these problems particularly difficult. This study reviews various approaches to handling constraints in metaheuristic optimization algorithms, with a focus on particle swarm optimization (PSO). The proposed PSO approach incorporates a mutation operator to enhance the diversity of the search process and a penalty function to manage constraints effectively. Several benchmark problems from the literature are used to assess the performance of the proposed technique. The outcomes are compared with those of various PSO variations and conventional optimization algorithms, demonstrating competitive performance by improving both convergence speed and solution quality. The analysis reveals that, in terms of convergence time and solution quality, the proposed method outperforms previous PSO variations and compares favorably with state-of-the-art optimization techniques. With potential applications across numerous fields, the proposed PSO method represents a promising approach to solving constraint-based optimization problems.