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Optimization of Nonlinear Mechanical Systems under Constraints with the Particle Swarm Method
Author(s) -
Sedlaczek Kai,
Eberhard Peter
Publication year - 2004
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.200410066
Subject(s) - mathematical optimization , particle swarm optimization , simulated annealing , augmented lagrangian method , computation , computer science , multi swarm optimization , nonlinear system , metaheuristic , flocking (texture) , optimization problem , nonlinear programming , evolutionary computation , derivative free optimization , algorithm , mathematics , materials science , physics , quantum mechanics , composite material
In recent years, stochastic optimization methods have gained increasing attention in parameter optimization of mechanical systems. Most popular techniques are Evolutionary Computation and the Simulating Annealing algorithm, which are applied more frequently to mechanical problems due to the increasing computing resources available now. Since theses methods do not require any gradient information, they are well suited for non‐smooth or discontinuous optimization tasks occurring in nonlinear multibody systems. In addition to these techniques, Kennedy and Eberhart [5] introduced the Particle Swarm Optimization method (PSO) based on the simulation of bird flocking. In this work, the efficiency of an extended PSO algorithm has been compared with an Evolutionary Strategy (ES) [6] and an Adapted Simulated Annealing method (ASA) [4]. In order to solve optimization tasks with both equality and inequality constraints the PSO algorithm has been extended by the Augmented Lagrangian Multiplier Method [2]. The proposed method shows often superior results and is quite simple to implement. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)