Aplicações do Algoritmo de Otimização por Enxame de Partículas para problemas com restrições

Abstract

This paper introduces a particle swarm optimization algorithm (PSO) that addresses nonlinear programming problems (NLP) with equality and inequality constraints. It introduces a metric called the Infeasibility Degree (IFD), which assesses how far solutions are from fully satisfying the constraints. The IFD is calculated as the sum of the squared violation values of the constraints. The proposed PSO algorithm performs simultaneous updates in the best local and global positions, considering both the objective value and the IFD. Results from a series of numerical tests, as well as the application of the algorithm to a challenging real-world engineering optimization problem, demonstrate the effectiveness of the proposed approach. The obtained results showcase significant potential for the practical application of this technique in various fields, including engineering, computer science, and more. The ability to effectively balance the search for the global optimum with ensuring that solutions respect the constraints makes this algorithm a valuable optimization tool.