A connectivity table for cluster similarity checking in the evolutionary optimization method

Cheng, Longjiu; Cai, Wensheng; Shao, Xueguang

doi:10.1016/j.cplett.2004.03.125

Cited by 54 publications

(64 citation statements)

References 28 publications

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“…comparingŨ (sons) toŨ (parents). Recent developments include similarity checking among cluster structure to keep the diversity of the population as the genetic optimization goes on, as in Cheng et al (2004). The use of genetic algorithms in cluster optimization was pioneered by Hartke (1993) and by Xiao and Williams (1993), who made applications to Si 4 and various molecular clusters respectively.…”

Section: Global Optimization Methodsmentioning

confidence: 99%

Structural properties of nanoclusters: Energetic, thermodynamic, and kinetic effects

Baletto¹,

Ferrando²

2005

Rev. Mod. Phys.

1,675

1,498

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The structural properties of free nanoclusters are reviewed. Special attention is paid to the interplay of energetic, thermodynamic and kinetic factors in the explanation of the clusters structures which are actually observed in the experiments. The review starts with a brief summary of the experimental methods for the production of free nanoclusters, and then proceeds with a guideline given by theoretical and simulation issues, always discussed in close connection with the experimental results. The energetic properties are treated first, evidencing general trends, describing the methods for modelling the interactions between the elementary cluster constituents, and for the global optimization on the cluster potential energy surface. After that, a chapter on cluster thermodynamics follows. The discussion includes the analysis of solid-solid structural transitions, and of melting with its size dependence. The last part is devoted to the growth kinetics of free nanoclusters, and treats the growth of isolated clusters and their coalescence. Several specific systems are analyzed.

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Section: Global Optimization Methodsmentioning

confidence: 99%

Structural properties of nanoclusters: Energetic, thermodynamic, and kinetic effects

Baletto¹,

Ferrando²

2005

Rev. Mod. Phys.

1,675

1,498

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“…52 With connectivity table technique for similarity checking, AIOA was also used in the optimization of large LJ clusters. 53 Clearly, optimization of bimetallic clusters is more complicated than homoatom clusters such as LJ clusters because of the ''homotop'' problem. 45 Both geometrical structure isomers and homotopic isomers should be considered in the optimization.…”

Section: Introductionmentioning

confidence: 99%

Optimization of bimetallic Cu–Au and Ag–Au clusters by using a modified adaptive immune optimization algorithm

Cai

Shao

2009

J Comput Chem

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A modified adaptive immune optimization algorithm (AIOA) is designed for optimization of Cu-Au and Ag-Au bimetallic clusters with Gupta potential. Compared with homoatom clusters, there are homotopic isomers in bimetallic cluster, so atom exchange operation is presented in the modified AIOA. The efficiency of the algorithm is tested by optimization of Cu(n)Au(38-n) (0 < or = n < or = 38). Results show that all the structures with the putative global minimal energies are successfully located. In the optimization of Ag(n)Au(55-n) (0 < or = n < or = 55) bimetallic clusters, all the structures with the reported minimal energies are obtained, and 36 structures with even lower potential energies are found. On the other hand, with the optimized structures of Cu(n)Au(55-n), it is shown that all 55-atom Cu-Au bimetallic clusters are Mackay icosahedra except for Au55, which is a face-centered cubic (fcc)-like structure; Cu55, Cu12Au43, and Cu1Au54 have two-shell Mackay icosahedral geometries with Ih point group symmetry.

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“…Recently, several novel algorithms aiming at LJ clusters optimization problem have been proposed, such as fast annealing evolutionary algorithm [25], conformational space annealing method [30], adaptive immune optimization algorithm [31], cluster similarity checking method [32], and so forth. These algorithms consider more about the special information about LJ clusters and perform better than CEO.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Continuous extremal optimization for Lennard-Jones clusters

et al. 2005

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In this paper, we explore a general-purpose heuristic algorithm for finding high-quality solutions to continuous optimization problems. The method, called continuous extremal optimization(CEO), can be considered as an extension of extremal optimization(EO) and is consisted of two components, one is with responsibility for global searching and the other is with responsibility for local searching. With only one adjustable parameter, the CEO's performance proves competitive with more elaborate stochastic optimization procedures. We demonstrate it on a well known continuous optimization problem: the Lennerd-Jones clusters optimization problem.

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A connectivity table for cluster similarity checking in the evolutionary optimization method

Cited by 54 publications

References 28 publications

Structural properties of nanoclusters: Energetic, thermodynamic, and kinetic effects

Structural properties of nanoclusters: Energetic, thermodynamic, and kinetic effects

Optimization of bimetallic Cu–Au and Ag–Au clusters by using a modified adaptive immune optimization algorithm

Continuous extremal optimization for Lennard-Jones clusters

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