Vibrational analysis of double-walled silicon carbide nano-cones: a finite element investigation

Abstract A three-dimensional finite element model is used to investigate the vibrational properties of double-walled silicon carbide nano-cones with various dimensions. The dependence of the vibrational properties of double-walled silicon carbide nano-cones on their length, apex angles and boundary conditions are evaluated. Current model consists a combination of beam and spring elements that simulates the interatomic interactions of bonding and nonbonding. The Lennard–Jones potential is employed to model the interactions between two non-bonding atoms. The fundamental frequency and mode shape of the double-walled silicon carbide nano-cones are calculated

Constructive cooperative coevolution for large-scale global optimisation

This paper presents the Constructive Cooperative Coevolutionary ( C3C3 ) algorithm, applied to continuous large-scale global optimisation problems. The novelty of C3C3 is that it utilises a multi-start architecture and incorporates the Cooperative Coevolutionary algorithm. The considered optimisation problem is decomposed into subproblems. An embedded optimisation algorithm optimises the subproblems separately while exchanging information to co-adapt the solutions for the subproblems. Further, C3C3 includes a novel constructive heuristic that generates different feasible solutions for the entire problem and thereby expedites the search. In this work, two different versions of C3C3 are evaluated on high-dimensional benchmark problems, including the CEC'2013 test suite for large-scale global optimisation. C3C3 is compared with several state-of-the-art algorithms, which shows that C3C3 is among the most competitive algorithms. C3C3 outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that C3C3 is an effective algorithm for large-scale global optimisation. This paper demonstrates the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative Coevolutionary algorithms in a multi-start framework