TY - GEN
T1 - Optimization of composite structures using bio-inspired methods
AU - Poteralski, Arkadiusz
AU - Szczepanik, Mirosław
AU - Beluch, Witold
AU - Burczyński, Tadeusz
PY - 2014
Y1 - 2014
N2 - The paper deals with an application of the artificial immune system (AIS) and the particle swarm optimizer (PSO) to the optimization problems. The AIS and PSO are applied to optimize of stacking sequence of plies in composites. The optimization task is formulated as maximization of minimal difference between the first five eigenfrequencies and the external excitation frequency. Recently, immune and swarm methods have found various applications in mechanics, and also in structural optimization. The AIS is a computational adaptive system inspired by the principles, processes and mechanisms of biological immune systems. The algorithms typically use the characteristics of the immune systems like learning and memory to simulate and solve a problem in a computational manner. The swarm algorithms are based on the models of the animals social behaviours: moving and living in the groups. The main advantage of the AIS and PSO, contrary to gradient methods of optimization, is the fact that they do not need any information about the gradient of fitness function. The numerical examples demonstrate that the new method based on immune and particle computation is an effective technique for solving computer aided optimal design.
AB - The paper deals with an application of the artificial immune system (AIS) and the particle swarm optimizer (PSO) to the optimization problems. The AIS and PSO are applied to optimize of stacking sequence of plies in composites. The optimization task is formulated as maximization of minimal difference between the first five eigenfrequencies and the external excitation frequency. Recently, immune and swarm methods have found various applications in mechanics, and also in structural optimization. The AIS is a computational adaptive system inspired by the principles, processes and mechanisms of biological immune systems. The algorithms typically use the characteristics of the immune systems like learning and memory to simulate and solve a problem in a computational manner. The swarm algorithms are based on the models of the animals social behaviours: moving and living in the groups. The main advantage of the AIS and PSO, contrary to gradient methods of optimization, is the fact that they do not need any information about the gradient of fitness function. The numerical examples demonstrate that the new method based on immune and particle computation is an effective technique for solving computer aided optimal design.
KW - artificial immune system
KW - composite
KW - finite element method
KW - laminate
KW - material constants
KW - modal analysis
KW - optimization
KW - particle swarm optimizer
UR - https://www.scopus.com/pages/publications/84902589996
U2 - 10.1007/978-3-319-07176-3_34
DO - 10.1007/978-3-319-07176-3_34
M3 - Conference contribution
AN - SCOPUS:84902589996
SN - 9783319071756
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 385
EP - 395
BT - Artificial Intelligence and Soft Computing - 13th International Conference, ICAISC 2014, Proceedings
PB - Springer Verlag
T2 - 13th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2014
Y2 - 1 June 2014 through 5 June 2014
ER -