Multi-Objective Particle Swarm Optimization for Distributed Generation Siting

This paper describes the application of multi-objective particle swarm optimization (MOPSO) for optimizing distributed generation siting locations. The algorithm implementation successfully achieves optimal objectives by maintaining an external archive of non-dominated solutions and using crowding distance for diversity preservation. Through its application, the method effectively considers multiple objectives including cost minimization, efficiency maximization, and environmental impact reduction, with validation across multiple experimental scenarios. The algorithm features adaptive mechanisms that continuously adjust inertia weights and acceleration coefficients through velocity update equations, enabling gradual convergence toward Pareto-optimal solutions. Key functions include particle position updates using social and cognitive components, and fitness evaluation through objective function computations. In summary, MOPSO proves to be an effective computational tool that plays a significant role in solving complex optimization problems like distributed generation siting, where it handles conflicting objectives through swarm intelligence principles and evolutionary computation techniques.