Optimization of Lennard-Jones clusters by particle swarm optimization with quasi-physical strategy

Mai, Guizhen; Hong, Yinghan; Fu, Shen; Lin, Yingqing; Zhang, Hao; Huang, Han; Zhu, Yuanhao

doi:10.1016/j.swevo.2020.100710

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…Bare LJ clusters are a class of well-studied systems and are usually considered as benchmarks for global optimization problems leading to structure prediction. Atomic clusters of size up to ∼1500 atoms are investigated in the past using different metaheuristic techniques. ,, PSO and its variants, like the improved CALYPSO code for nonperiodic systems, have proved to be fast and reliable in predicting the LJ cluster geometries. ,− Reports from various global optimization calculations have shown that the LJ clusters usually adopt an icosahedral geometry. , By employing PSO for the global optimization of the LJ clusters followed by a local optimization using L-BFGS (PSO-L-BFGS), we were able to reproduce the literature results for bare LJ clusters of He, Ne, and Ar of various sizes. Upon confinement within a void or upon adsorption on a substrate, such LJ clusters have been reported to exhibit completely different geometries than the bare clusters.…”

Section: Resultsmentioning

confidence: 90%

Swarm Intelligence Steers a Global Minima Search of Clusters Bound on Carbon Nanostructures

et al. 2020

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A detailed exploration of the energy landscape is a necessary criterion for an efficient structure prediction. Although computational power has increased many-folds in recent decades, a full analysis of the potential energy surfaces (PESs) for complex chemical systems is still computationally elusive. Locating the most stable geometry of a system from a highly complex PES necessitates the use of metaheuristic techniques. Particle swarm optimization (PSO), a nature-inspired metaheuristic technique falling under the general ambit of artificial intelligence, has proven to be a promising approach for structure prediction. Through social networking, the swarm attains a collective intelligence leading to an effective search of the PESs for the most stable configurations. The PESs associated with the noncovalent interactions involving carbon nanostructures and atomic or molecular clusters are often rugged, with a large number of local minima hindering the tracking down of the global minima. In this Perspective, we demonstrate that an amalgamation of PSO with the continuum approximation, an approximation that exploits the symmetry of carbon nanostructures, serves as a robust methodology for unraveling the interactions of Lennard-Jones clusters with carbon nanostructures. The putative global minima geometries obtained from our methodology can provide fruitful insights on the structural and energetic aspects of cluster binding on carbon nanostructures and can offer excellent initial configurations for first-principles calculations.

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Section: Resultsmentioning

confidence: 90%

Swarm Intelligence Steers a Global Minima Search of Clusters Bound on Carbon Nanostructures

et al. 2020

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“…PSO algorithm is inspired by a flock of birds seeking food. It treats each solution of the optimization problem as a bird that flies at a certain velocity in the search space, and its velocity is adjusted dynamically [38][39][40]. The bird is abstracted as a particle without weight and volume, and the location of the i-th particle in all the n dimensions is represented as…”

Section: Canonical Psomentioning

confidence: 99%

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Geng

Chen

Nguyen

et al. 2021

Complex Intell. Syst.

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This paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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“…A population initialization method based on orthogonal array is proposed to speed up the early convergence of the algorithm [17]. A quasi-physical strategy-based PSO algorithm (QPS-PSO) is proposed [18], which improves the optimization model of the PSO algorithm, and triggers the specified convergence model according to fitness, and solves the problem of premature convergence and convergence to local optimal in solving multi-modal global optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Surface Roughness for Titanium Alloy Based on Multi- strategy Fusion Snake Algorithm

Zhao,

Li,

Zhao

et al. 2024

Preprint

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Titanium alloy has the characteristics of low thermal conductivity, small elastic modulus, and serious work hardening, which makes it difficult to predict the surface quality after high-speed milling. Surface quality is known to significantly impact the wear resistance, fatigue strength, and corrosion resistance of parts. To improve the service performance of titanium alloy parts, it is of great significance to optimize the milling parameters based on the improvement of surface quality. Therefore, this paper proposes a milling parameter optimization method based on the snake algorithm based on multi-strategy fusion. The surface roughness was used as the optimization goal to optimize the parameters. Firstly, the response surface method was used to establish a prediction model of titanium alloy milling surface roughness to realize the prediction of surface roughness and make it continuous. Then, the snake algorithm with multi-strategy fusion was proposed, which initialized the population based on the orthogonal matrix initialization strategy, so that the population individuals were more evenly distributed in space, increased the diversity of the population, improved the model of food quantity and temperature in the algorithm, optimized the change mechanism of food quantity and temperature in the original algorithm into a dynamic adaptive mechanism, accelerated the convergence speed, used the joint reverse strategy to select and generate individuals with higher fitness, and strengthened the ability of the algorithm to escape the local optimal solution. Experimental results on five benchmarks with multiple comparative optimization algorithms show that the MSSO algorithm has faster convergence speed and higher convergence accuracy. Finally, the multi-strategy snake algorithm was used to optimize the optimization objective equation, and the milling parameter experiment shows that the surface roughness of Ti64 is increased by 55.7 percent compared with that before optimization, and the surface roughness of the specimen optimized by the multi-strategy fusion snake algorithm is significantly reduced, the surface toolpath row spacing is reduced, and the average height of the texture is reduced. This method can reduce the optimization time and ensure better optimization results than the classical optimization algorithm.

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Optimization of Lennard-Jones clusters by particle swarm optimization with quasi-physical strategy

Cited by 7 publications

References 33 publications

Swarm Intelligence Steers a Global Minima Search of Clusters Bound on Carbon Nanostructures

Swarm Intelligence Steers a Global Minima Search of Clusters Bound on Carbon Nanostructures

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Optimization of Surface Roughness for Titanium Alloy Based on Multi- strategy Fusion Snake Algorithm

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