An improved genetic algorithm for structural optimization of Au–Ag bimetallic nanoparticles

Shao, Guifang; Shangguan, Yali; Tao, Jiping; Zheng, Jian‐Guo; Liu, Tundong; Ye, Wen

doi:10.1016/j.asoc.2018.08.019

Cited by 22 publications

(8 citation statements)

References 27 publications

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“…As discussed above, multiple categories of meta-heuristic algorithms have been used to solve FJSP successfully [ 26 ]. Due to the better performance and greater generality of GA, it has gained great attention [ 27 ]. Furthermore, extensive studies have demonstrated that GA has superior performance for the quality of the solution.…”

Section: Literature Reviewmentioning

confidence: 99%

A Self-Adjusting Search Domain Method-Based Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem

Xia

2022

Computational Intelligence and Neuroscience

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As a nondeterministic polynomial (NP) problem, the flexible job shop scheduling problem (FJSP) is a difficult problem to be solved in terms of finding an acceptable solution. In last decades, genetic algorithm (GA) displays very promising performance in the field. In this article, a hybrid algorithm combining global and local search with reinitialization (GLRe)-based GA is proposed to minimize makespan for FJSP. The solution of FJSP is conveniently represented by a double-layer chromosome representation method, which is convenient for subsequent genetic operations, that is, sorting of operations and selection of machines. Two strategies of choosing the job with the most remaining operations (CRO) and 6-dimensional variable determined search position (6D-VSP) are proposed as two components for GA, which are applied to generate a population with superior quality and reduce the global search space during the initialization stage. At the same time, in order to prevent the loss of diversity during evolution, a reinitialization strategy is introduced in the later stage of evolution to adaptively adjust the search domain of the problem. Finally, two sets of benchmark data are tested. The experimental results demonstrate the accuracy and effectiveness of the GLRe proposed in this article for solving FJSP.

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Section: Literature Reviewmentioning

confidence: 99%

A Self-Adjusting Search Domain Method-Based Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem

Xia

2022

Computational Intelligence and Neuroscience

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“…Therefore, many researchers have started to develop more efficient heuristics to obtain near-optimal solutions 5 . Among them, the genetic algorithm (GA) is one of the most commonly used algorithms to solve FJSP because of its superior performance and strong generality 6 . Chang et al 7 proposed a hybrid Taguchi-genetic algorithm to solve FJSP, which embeds the Taguchi method after mating to improve the effectiveness of the GA. Chen et al 8 presented a self-learning genetic algorithm (SLGA) in which GA is the basic optimization method and its key parameters are intelligently tuned based on reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

A heuristic optimization approach for ship block painting operation scheduling

Liu

Jiang

Niu

et al. 2023

Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022)

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The workload of the block painting stage accounts for the largest proportion of the ship painting, so its operation scheduling directly affects the painting quality and efficiency. In this paper, the scheduling problem of ship block painting operation is investigated. The problem can be regarded as a variant of the flexible job-shop scheduling problem. The mathematical model is developed considering a variety of practical constraints, including space size constraints, multiple spray-painting constraints, and waiting time constraints between processes. To effectively solve the problem, we propose a hybrid meta-heuristic to combine the merits of genetic algorithm and tabu search. We conduct numerical experiments on actual production data instances of different scales. The results show that our hybrid approach has significant advantages over the baseline algorithms.

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“…), it has good convergence speed and can be directly used for discrete problems such as feature selection. It has advantages of robustness and strong scalability, however is also complicated in programming, parameter tunings, and slow search speed [20]. The particle swarm optimization (PSO) algorithm on the other hand, has a fairly fast approximation speed of the optimal solution, which can effectively optimize the parameters of the system [13].…”

Section: Introductionmentioning

confidence: 99%

Echo state network optimization using binary grey wolf algorithm

et al. 2020

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The echo state network (ESN) is a powerful recurrent neural network for time series modelling. ESN inherits the simplified structure and relatively straightforward training process of conventional neural networks, and shows strong computational capabilities to solve nonlinear problems. It is able to map low-dimensional input signals to high-dimensional space for information extraction, but it is found that not every dimension of the reservoir output directly contributes to the model generalization. This work aims to improve the generalization capabilities of the ESN model by reducing the redundant reservoir output features. A novel hybrid model, namely binary grey wolf echo state network (BGWO-ESN), is proposed which optimises the ESN output connection by the feature selection scheme. Specially, the feature selection scheme of BGWO is developed to improve the ESN output connection structure. The proposed method is evaluated using synthetic and financial data sets. Experimental results demonstrate that the proposed BGWO-ESN model is more effective than other benchmarks, and obtains the lowest generalization error.

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An improved genetic algorithm for structural optimization of Au–Ag bimetallic nanoparticles

Cited by 22 publications

References 27 publications

A Self-Adjusting Search Domain Method-Based Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem

A Self-Adjusting Search Domain Method-Based Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem

A heuristic optimization approach for ship block painting operation scheduling

Echo state network optimization using binary grey wolf algorithm

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