Genetic Algorithms in Application to the Geometry Optimization of Nanoparticles

Abstract

Applications of genetic algorithms to the global geometry optimization problem of nanoparticles are reviewed. Genetic operations are investigated and importance of phenotype genetic operations, considering the geometry of nanoparticles, are mentioned. Other efficiency improving developments such as floating point representation and local relaxation are described broadly. Parallelization issues are also considered and a recent parallel working single parent Lamarckian genetic algorithm is reviewed with applications on carbon clusters and SiGe core-shell structures.

Structural optimization of Pt–Pd–Rh trimetallic nanoparticles using improved genetic algorithm

“…Therefore, the global structural optimization of trimetallic NPs becomes a non-polynomial-complete problem and more difficult compared with bimetallic NPs [36]. In this article, the GA is employed to solve this combinatorial optimization problem, and this algorithm has been proved to be effective [37,38]. In addition, to effectively solve the problems mentioned above, a layered coordinate ranking method and an effective fitness function were introduced into the classical GA procedure.…”

Structural studies of Au–Pd bimetallic nanoparticles by a genetic algorithm method

“…Theoretically, the global optimization for searching the stable structures of nanoparticles is a nonpolynomial-complete problem [34]. In this paper, we employed a genetic algorithm (GA) to solve this combinatorial optimization problem, and this algorithm has been proved effectiveness [35,36]. In general, a classical GA procedure mainly includes initialization of population, calculation of fitness, selection, crossover and mutation.…”

The saturation of population fitness as a stopping criterion in genetic algorithm

“…GA has been categorised under the family of meta-heuristic algorithms [15,16] such as Tabu Search and Artificial Neural Network. Meta-heuristic algorithms are always used for solving combinatorial problems or hard optimization problems [17] since they can provide good solutions at reasonable computational cost [18,19]. However, they may not be able to guarantee the optimality of solution due to their stochastic nature [20].…”