Optimization of composite structures using bio-inspired methods / Arkadiusz Poteralski, Mirosław Szczepanik, Witold Beluch, Tadeusz Burczyński // W: Artificial Intelligence and Soft Computing : 13th ... więcej

Optimization of composite structures using bio-inspired methods / Arkadiusz Poteralski, Mirosław Szczepanik, Witold Beluch, Tadeusz Burczyński // W: Artificial Intelligence and Soft Computing : 13th International Conference, ICAISC 2014, Zakopane, Poland, June 1-5, 2014 : proceedings. Pt. 2 [Dokument elektroniczny] / eds. Leszek Rutkowski [et al.]. – Cham [etc.] : Springer, 2014. – (Lecture Notes in Artificial Intelligence. – S. 385-395 : rys., schem., tab. – Tryb dostępu: https://link.springer.com/chapter/10.1007/978-3-319-07176-3_34. – Bibliogr. 25 poz., Abstr. – Seria gł.: Lecture Notes in Computer Science, ISSN 1611-3349 ; 8468. – doi: 10.1007/978-3-319-07176-3_34. – ISBN 978-3-319-07176-3 (online). – ISBN 978-3-319-07175-6 (print)

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 ... więcej

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.