Um algoritmo por enxame de partículas para a solução de problemas de otimização estrutural multiobjetivo considerando frequências naturais de vibração

Abstract

The interest in multiobjective optimization algorithms has grown in recent years due to its applicability in problems from several areas, especially those from engineering. In general, the objectives considered in these problems are conflicting and a Pareto Front curve composed of the non-dominated solutions is expected as the solution to such problems. In the context of evolutionary computation there are many of algorithms applied to this type of problem, such as genetic algorithms, differential evolution, and particle swarm. This study aims to evaluate the ability of a multiobjective particle swarm algorithm, called Multiobjective Craziness based Particle Swarm Optimization (MOCRPSO) in a set of unconstrained multiobjective structural optimization problems. The problem consists to minimize the mass of structures, considering as second objective the displacement of the nodes or the natural frequencies of vibration of the strucuture. Additionally, cardinality constraints are adopted in order to obtain an automatic variable linking, searching for the best member grouping of the bars. An Adaptive Penalty Method (APM), which has been successfully applied to solving mono and multiobjective optimization problems, is used here to handle the constraints. To investigate the performance of the proposed method, six test-problems with and without constraints and six truss structures are analyzed and the results obtained illustrate its efficiency when compared to other algorithms from the literature.